From 1025d23af9f7893762d93a140fce6f4321e550de Mon Sep 17 00:00:00 2001
From: Brian Hou <bhou1@cs.washington.edu>
Date: Tue, 18 May 2021 14:22:56 -0700
Subject: [PATCH] Release Project 4: Planning.
---
control/CMakeLists.txt | 8 +
control/src/control/controller.py | 7 +
control/src/control/mpc.py | 5 -
cse478/src/cse478/utils.py | 29 +-
planning/CMakeLists.txt | 43 +++
planning/config/planning.rviz | 353 ++++++++++++++++++++
planning/launch/make_roadmap.launch | 29 ++
planning/launch/planner_sim.launch | 45 +++
planning/package.xml | 47 +++
planning/scripts/planner | 15 +
planning/scripts/roadmap | 69 ++++
planning/scripts/run_search | 82 +++++
planning/setup.py | 9 +
planning/src/planning/__init__.py | 0
planning/src/planning/dubins.py | 313 ++++++++++++++++++
planning/src/planning/planner_ros.py | 259 +++++++++++++++
planning/src/planning/problems.py | 183 +++++++++++
planning/src/planning/roadmap.py | 357 ++++++++++++++++++++
planning/src/planning/samplers.py | 146 +++++++++
planning/src/planning/search.py | 146 +++++++++
planning/test/astar.py | 184 +++++++++++
planning/test/problems.py | 110 +++++++
planning/test/roadmap.py | 185 +++++++++++
planning/test/samplers.py | 278 ++++++++++++++++
planning/test/share/map1.txt | 9 +
planning/test/share/map2.txt | 473 +++++++++++++++++++++++++++
planning/test/shortcut.py | 64 ++++
planning/writeup/README.md | 4 +
28 files changed, 3445 insertions(+), 7 deletions(-)
create mode 100644 planning/CMakeLists.txt
create mode 100644 planning/config/planning.rviz
create mode 100644 planning/launch/make_roadmap.launch
create mode 100644 planning/launch/planner_sim.launch
create mode 100644 planning/package.xml
create mode 100755 planning/scripts/planner
create mode 100755 planning/scripts/roadmap
create mode 100755 planning/scripts/run_search
create mode 100644 planning/setup.py
create mode 100644 planning/src/planning/__init__.py
create mode 100644 planning/src/planning/dubins.py
create mode 100644 planning/src/planning/planner_ros.py
create mode 100644 planning/src/planning/problems.py
create mode 100644 planning/src/planning/roadmap.py
create mode 100644 planning/src/planning/samplers.py
create mode 100644 planning/src/planning/search.py
create mode 100644 planning/test/astar.py
create mode 100644 planning/test/problems.py
create mode 100644 planning/test/roadmap.py
create mode 100644 planning/test/samplers.py
create mode 100644 planning/test/share/map1.txt
create mode 100644 planning/test/share/map2.txt
create mode 100644 planning/test/shortcut.py
create mode 100644 planning/writeup/README.md
diff --git a/control/CMakeLists.txt b/control/CMakeLists.txt
index b2e95f8..2b0bf63 100644
--- a/control/CMakeLists.txt
+++ b/control/CMakeLists.txt
@@ -28,6 +28,14 @@ catkin_package(
std_msgs
)
+install(
+ DIRECTORY
+ config
+ launch
+ DESTINATION
+ ${CATKIN_PACKAGE_SHARE_DESTINATION}
+)
+
catkin_install_python(
PROGRAMS
scripts/control_node
diff --git a/control/src/control/controller.py b/control/src/control/controller.py
index 8196160..ba9101a 100644
--- a/control/src/control/controller.py
+++ b/control/src/control/controller.py
@@ -318,6 +318,13 @@ def time_parameterize_ramp_up_ramp_down(path_xyt, speed):
Returns:
path_xytv: np.array of states and speed with shape L x 4
"""
+ # For paths with fewer waypoints than necessary to ramp up and ramp down,
+ # just set the desired speed directly (with a non-zero final speed, to
+ # guarantee forward movement).
+ if path_xyt.shape[0] < 4:
+ speeds = speed * np.ones(path_xyt.shape[0])
+ return np.hstack([path_xyt, speeds[:, np.newaxis]])
+
ramp_distance = 0.5
displacement_vectors = np.diff(path_xyt[:, [0, 1]], axis=0)
displacements = np.linalg.norm(displacement_vectors, axis=1)
diff --git a/control/src/control/mpc.py b/control/src/control/mpc.py
index 9c020c1..5d378fb 100644
--- a/control/src/control/mpc.py
+++ b/control/src/control/mpc.py
@@ -223,11 +223,6 @@ class ModelPredictiveController(BaseController):
self.bbox_map = np.zeros((self.K * self.T, 2, 4))
self.collisions = np.zeros(self.K * self.T, dtype=bool)
self.obstacle_map = ~self.permissible_region
- self.map_x = self.map_info.origin.position.x
- self.map_y = self.map_info.origin.position.y
- self.map_angle = utils.quaternion_to_angle(self.map_info.origin.orientation)
- self.map_c = np.cos(self.map_angle)
- self.map_s = np.sin(self.map_angle)
car_l = self.car_length
car_w = self.car_width
self.car_bbox = (
diff --git a/cse478/src/cse478/utils.py b/cse478/src/cse478/utils.py
index bb019ac..3390008 100644
--- a/cse478/src/cse478/utils.py
+++ b/cse478/src/cse478/utils.py
@@ -2,9 +2,10 @@
"""ROS geometry and map utility functions."""
from __future__ import division
+
+import heapq
import numpy as np
import rospy
-
from geometry_msgs.msg import (
Pose,
PoseStamped,
@@ -150,7 +151,7 @@ def rotation_matrix(theta):
The equivalent 2x2 rotation matrix as a np.array
"""
c, s = np.cos(theta), np.sin(theta)
- return np.matrix([[c, -s], [s, c]])
+ return np.array([[c, -s], [s, c]])
def particle_to_pose(particle):
@@ -342,3 +343,27 @@ def estimation_error(estimates, references):
np.cos(reference_angles - estimate_angles),
)
return position_errors, np.abs(angular_errors)
+
+
+class PriorityQueue(object):
+ def __init__(self):
+ self.min_heap = []
+ self.pop_counter = 0
+
+ def __len__(self):
+ return len(self.min_heap)
+
+ def push(self, elem):
+ heapq.heappush(self.min_heap, elem)
+
+ def peek(self):
+ if not self.min_heap:
+ raise IndexError("no elements to peek")
+ return self.min_heap[0][1]
+
+ def pop(self):
+ if not self.min_heap:
+ raise IndexError("no elements to pop")
+ elem = heapq.heappop(self.min_heap)
+ self.pop_counter += 1
+ return elem
diff --git a/planning/CMakeLists.txt b/planning/CMakeLists.txt
new file mode 100644
index 0000000..c7ad448
--- /dev/null
+++ b/planning/CMakeLists.txt
@@ -0,0 +1,43 @@
+cmake_minimum_required(VERSION 3.0.2)
+project(planning)
+
+find_package(catkin REQUIRED COMPONENTS
+ rospy
+)
+
+catkin_python_setup()
+
+catkin_package()
+
+install(
+ DIRECTORY
+ launch
+ DESTINATION
+ ${CATKIN_PACKAGE_SHARE_DESTINATION}
+)
+
+catkin_install_python(
+ PROGRAMS
+ scripts/roadmap
+ scripts/run_search
+ scripts/planner
+ DESTINATION
+ ${CATKIN_PACKAGE_BIN_DESTINATION}
+)
+
+if(CATKIN_ENABLE_TESTING)
+ find_package(roslaunch REQUIRED)
+ # find_package(rostest REQUIRED)
+
+ roslaunch_add_file_check(launch)
+
+ # catkin_add_nosetests tests Python code directly
+
+ catkin_add_nosetests(test/samplers.py)
+ catkin_add_nosetests(test/roadmap.py)
+ catkin_add_nosetests(test/astar.py)
+ catkin_add_nosetests(test/shortcut.py)
+
+ # add_rostest tests the ROS integrations
+
+endif()
diff --git a/planning/config/planning.rviz b/planning/config/planning.rviz
new file mode 100644
index 0000000..a681f08
--- /dev/null
+++ b/planning/config/planning.rviz
@@ -0,0 +1,353 @@
+Panels:
+ - Class: rviz/Displays
+ Help Height: 78
+ Name: Displays
+ Property Tree Widget:
+ Expanded:
+ - /Map1/Orientation1
+ Splitter Ratio: 0.5
+ Tree Height: 648
+ - Class: rviz/Selection
+ Name: Selection
+ - Class: rviz/Tool Properties
+ Expanded:
+ - /2D Pose Estimate1
+ - /2D Nav Goal1
+ - /Publish Point1
+ Name: Tool Properties
+ Splitter Ratio: 0.5886790156364441
+ - Class: rviz/Views
+ Expanded:
+ - /Current View1
+ Name: Views
+ Splitter Ratio: 0.5
+ - Class: rviz/Time
+ Experimental: false
+ Name: Time
+ SyncMode: 0
+ SyncSource: LaserScan
+Preferences:
+ PromptSaveOnExit: true
+Toolbars:
+ toolButtonStyle: 2
+Visualization Manager:
+ Class: ""
+ Displays:
+ - Alpha: 0.5
+ Cell Size: 1
+ Class: rviz/Grid
+ Color: 0; 0; 0
+ Enabled: true
+ Line Style:
+ Line Width: 0.029999999329447746
+ Value: Lines
+ Name: Grid
+ Normal Cell Count: 0
+ Offset:
+ X: 0
+ Y: 0
+ Z: 0
+ Plane: XY
+ Plane Cell Count: 400
+ Reference Frame: <Fixed Frame>
+ Value: true
+ - Alpha: 0.30000001192092896
+ Class: rviz/Map
+ Color Scheme: map
+ Draw Behind: true
+ Enabled: true
+ Name: Map
+ Topic: /map
+ Unreliable: false
+ Use Timestamp: false
+ Value: true
+ - Alpha: 1
+ Class: rviz/RobotModel
+ Collision Enabled: false
+ Enabled: true
+ Links:
+ All Links Enabled: true
+ Expand Joint Details: false
+ Expand Link Details: false
+ Expand Tree: false
+ Link Tree Style: Links in Alphabetic Order
+ back_left/wheel_link:
+ Alpha: 1
+ Show Axes: false
+ Show Trail: false
+ Value: true
+ back_right/wheel_link:
+ Alpha: 1
+ Show Axes: false
+ Show Trail: false
+ Value: true
+ base_footprint:
+ Alpha: 1
+ Show Axes: false
+ Show Trail: false
+ base_link:
+ Alpha: 1
+ Show Axes: false
+ Show Trail: false
+ Value: true
+ camera_bottom_screw_frame:
+ Alpha: 1
+ Show Axes: false
+ Show Trail: false
+ camera_link:
+ Alpha: 1
+ Show Axes: false
+ Show Trail: false
+ Value: true
+ front_left/wheel_link:
+ Alpha: 1
+ Show Axes: false
+ Show Trail: false
+ Value: true
+ front_left/wheel_steer_link:
+ Alpha: 1
+ Show Axes: false
+ Show Trail: false
+ front_right/wheel_link:
+ Alpha: 1
+ Show Axes: false
+ Show Trail: false
+ Value: true
+ front_right/wheel_steer_link:
+ Alpha: 1
+ Show Axes: false
+ Show Trail: false
+ insets_link:
+ Alpha: 1
+ Show Axes: false
+ Show Trail: false
+ laser_link:
+ Alpha: 1
+ Show Axes: false
+ Show Trail: false
+ Value: true
+ platform_link:
+ Alpha: 1
+ Show Axes: false
+ Show Trail: false
+ Name: RobotModel
+ Robot Description: car/robot_description
+ TF Prefix: ""
+ Update Interval: 0
+ Value: true
+ Visual Enabled: true
+ - Alpha: 1
+ Autocompute Intensity Bounds: true
+ Autocompute Value Bounds:
+ Max Value: 10
+ Min Value: -10
+ Value: true
+ Axis: Z
+ Channel Name: intensity
+ Class: rviz/LaserScan
+ Color: 255; 22; 144
+ Color Transformer: FlatColor
+ Decay Time: 0
+ Enabled: false
+ Invert Rainbow: false
+ Max Color: 255; 22; 144
+ Min Color: 255; 22; 144
+ Name: LaserScan
+ Position Transformer: XYZ
+ Queue Size: 10
+ Selectable: true
+ Size (Pixels): 3
+ Size (m): 0.029999999329447746
+ Style: Points
+ Topic: /car/scan
+ Unreliable: false
+ Use Fixed Frame: true
+ Use rainbow: true
+ Value: false
+ - Alpha: 1
+ Arrow Length: 0.30000001192092896
+ Axes Length: 0.30000001192092896
+ Axes Radius: 0.009999999776482582
+ Class: rviz/PoseArray
+ Color: 255; 25; 0
+ Enabled: true
+ Head Length: 0.07000000029802322
+ Head Radius: 0.029999999329447746
+ Name: Real Path
+ Queue Size: 10
+ Shaft Length: 0.23000000417232513
+ Shaft Radius: 0.009999999776482582
+ Shape: Arrow (Flat)
+ Topic: /car/controller/real_path/poses
+ Unreliable: false
+ Value: true
+ - Alpha: 1
+ Arrow Length: 0.30000001192092896
+ Axes Length: 0.30000001192092896
+ Axes Radius: 0.009999999776482582
+ Class: rviz/PoseArray
+ Color: 138; 226; 52
+ Enabled: true
+ Head Length: 0.07000000029802322
+ Head Radius: 0.029999999329447746
+ Name: Reference Path
+ Queue Size: 10
+ Shaft Length: 0.23000000417232513
+ Shaft Radius: 0.009999999776482582
+ Shape: Arrow (Flat)
+ Topic: /car/controller/path/poses
+ Unreliable: false
+ Value: true
+ - Alpha: 1
+ Axes Length: 1
+ Axes Radius: 0.10000000149011612
+ Class: rviz/Pose
+ Color: 0; 0; 255
+ Enabled: true
+ Head Length: 0.20000000298023224
+ Head Radius: 0.10000000149011612
+ Name: Reference State
+ Queue Size: 10
+ Shaft Length: 0.20000000298023224
+ Shaft Radius: 0.02500000037252903
+ Shape: Arrow
+ Topic: /car/controller/path/reference_state
+ Unreliable: false
+ Value: true
+ - Class: rviz/MarkerArray
+ Enabled: true
+ Marker Topic: /car/controller/rollouts
+ Name: Rollouts
+ Namespaces:
+ {}
+ Queue Size: 100
+ Value: true
+ - Alpha: 1
+ Axes Length: 1
+ Axes Radius: 0.10000000149011612
+ Class: rviz/Pose
+ Color: 255; 25; 0
+ Enabled: false
+ Head Length: 0.30000001192092896
+ Head Radius: 0.10000000149011612
+ Name: Pose
+ Queue Size: 10
+ Shaft Length: 1
+ Shaft Radius: 0.05000000074505806
+ Shape: Arrow
+ Topic: /move_base_simple/goal
+ Unreliable: false
+ Value: false
+ - Alpha: 1
+ Arrow Length: 0.30000001192092896
+ Axes Length: 0.30000001192092896
+ Axes Radius: 0.009999999776482582
+ Class: rviz/PoseArray
+ Color: 0; 0; 0
+ Enabled: true
+ Head Length: 0.07000000029802322
+ Head Radius: 0.10000000149011612
+ Name: Planning Vertices
+ Queue Size: 10
+ Shaft Length: 0.10000000149011612
+ Shaft Radius: 0.019999999552965164
+ Shape: Arrow (3D)
+ Topic: /car/planner/vertices
+ Unreliable: false
+ Value: true
+ - Class: rviz/Marker
+ Enabled: true
+ Marker Topic: /car/planner/edges
+ Name: Planning Edges
+ Namespaces:
+ "": true
+ Queue Size: 100
+ Value: true
+ Enabled: true
+ Global Options:
+ Background Color: 255; 255; 255
+ Default Light: true
+ Fixed Frame: map
+ Frame Rate: 30
+ Name: root
+ Tools:
+ - Class: rviz/Interact
+ Hide Inactive Objects: true
+ - Class: rviz/MoveCamera
+ - Class: rviz/Select
+ - Class: rviz/FocusCamera
+ - Class: rviz/Measure
+ - Class: rviz/SetInitialPose
+ Theta std deviation: 0.2617993950843811
+ Topic: /initialpose
+ X std deviation: 0.5
+ Y std deviation: 0.5
+ - Class: rviz/SetGoal
+ Topic: /move_base_simple/goal
+ - Class: rviz/PublishPoint
+ Single click: true
+ Topic: /clicked_point
+ Value: true
+ Views:
+ Current:
+ Angle: 0
+ Class: rviz/TopDownOrtho
+ Enable Stereo Rendering:
+ Stereo Eye Separation: 0.05999999865889549
+ Stereo Focal Distance: 1
+ Swap Stereo Eyes: false
+ Value: false
+ Invert Z Axis: false
+ Name: Current View
+ Near Clip Distance: 0.009999999776482582
+ Scale: 20
+ Target Frame: <Fixed Frame>
+ X: 20
+ Y: 40
+ Saved:
+ - Angle: 0
+ Class: rviz/TopDownOrtho
+ Enable Stereo Rendering:
+ Stereo Eye Separation: 0.05999999865889549
+ Stereo Focal Distance: 1
+ Swap Stereo Eyes: false
+ Value: false
+ Invert Z Axis: false
+ Name: TopDownOrtho
+ Near Clip Distance: 0.009999999776482582
+ Scale: 206.59109497070312
+ Target Frame: base_link
+ X: 0
+ Y: 0
+ - Angle: 0
+ Class: rviz/TopDownOrtho
+ Enable Stereo Rendering:
+ Stereo Eye Separation: 0.05999999865889549
+ Stereo Focal Distance: 1
+ Swap Stereo Eyes: false
+ Value: false
+ Invert Z Axis: false
+ Name: TopDownOrtho
+ Near Clip Distance: 0.009999999776482582
+ Scale: 20
+ Target Frame: <Fixed Frame>
+ X: 20
+ Y: 40
+Window Geometry:
+ Displays:
+ collapsed: false
+ Height: 945
+ Hide Left Dock: false
+ Hide Right Dock: false
+ QMainWindow State: 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
+ Selection:
+ collapsed: false
+ Time:
+ collapsed: false
+ Tool Properties:
+ collapsed: false
+ Views:
+ collapsed: false
+ Width: 1720
+ X: 72
+ Y: 27
diff --git a/planning/launch/make_roadmap.launch b/planning/launch/make_roadmap.launch
new file mode 100644
index 0000000..80d381d
--- /dev/null
+++ b/planning/launch/make_roadmap.launch
@@ -0,0 +1,29 @@
+<launch>
+ <arg name="car_name" default="car"/>
+ <arg name="map" default="$(find cse478)/maps/maze_0.yaml"/>
+ <arg name="initial_x" default="0"/>
+ <arg name="initial_y" default="0"/>
+ <arg name="initial_theta" default="0"/>
+ <arg name="fake_localization" default="false" />
+ <arg name="teleop" default="false"/>
+
+ <arg name="sampler" default="halton"/>
+ <arg name="problem" default="se2"/>
+ <arg name="num_vertices" default="200"/>
+ <arg name="connection_radius" default="100.0"/>
+ <arg name="curvature" default="0.05"/>
+
+ <include file="$(find cse478)/launch/teleop.launch">
+ <arg name="fake_localization" value="$(arg fake_localization)" />
+ <arg name="map" value="$(arg map)"/>
+ <arg name="initial_x" value="$(arg initial_x)"/>
+ <arg name="initial_y" value="$(arg initial_y)"/>
+ <arg name="initial_theta" value="$(arg initial_theta)"/>
+ <arg name="teleop" value="$(arg teleop)" />
+ </include>
+
+ <group ns="$(arg car_name)">
+ <node pkg="planning" type="roadmap" name="roadmap"
+ args="--use-ros-map --lazy --sampler $(arg sampler) --problem $(arg problem) --num-vertices $(arg num_vertices) --connection-radius $(arg connection_radius) --curvature $(arg curvature)" output="screen"/>
+ </group>
+</launch>
diff --git a/planning/launch/planner_sim.launch b/planning/launch/planner_sim.launch
new file mode 100644
index 0000000..508fdf2
--- /dev/null
+++ b/planning/launch/planner_sim.launch
@@ -0,0 +1,45 @@
+<launch>
+ <arg name="car_name" default="car"/>
+ <arg name="map" default="$(find cse478)/maps/maze_0.yaml"/>
+ <arg name="initial_x" default="0"/>
+ <arg name="initial_y" default="0"/>
+ <arg name="initial_theta" default="0"/>
+ <arg name="fake_localization" default="false" />
+ <arg name="controller" default="mpc"/>
+ <arg name="teleop" default="true"/>
+ <arg name="rviz" default="true" />
+
+ <arg name="num_vertices" default="200"/>
+ <arg name="connection_radius" default="100.0"/>
+ <arg name="curvature" default="0.05"/>
+
+ <include file="$(find cse478)/launch/teleop.launch">
+ <arg name="fake_localization" value="$(arg fake_localization)" />
+ <arg name="map" value="$(arg map)"/>
+ <arg name="initial_x" value="$(arg initial_x)"/>
+ <arg name="initial_y" value="$(arg initial_y)"/>
+ <arg name="initial_theta" value="$(arg initial_theta)"/>
+ <arg name="teleop" value="$(arg teleop)" />
+ </include>
+
+ <group ns="$(arg car_name)">
+ <include file="$(find localization)/launch/include/particle_filter.xml">
+ <arg name="publish_tf" value="$(eval not fake_localization)" />
+ <arg name="initial_x" value="$(arg initial_x)"/>
+ <arg name="initial_y" value="$(arg initial_y)"/>
+ <arg name="initial_theta" value="$(arg initial_theta)"/>
+ </include>
+
+ <node pkg="planning" type="planner" name="planner" output="screen">
+ <param name="num_vertices" value="$(arg num_vertices)"/>
+ <param name="connection_radius" value="$(arg connection_radius)"/>
+ <param name="curvature" value="$(arg curvature)"/>
+ </node>
+ </group>
+
+ <include file="$(find control)/launch/controller.launch">
+ <arg name="type" default="$(arg controller)"/>
+ </include>
+
+ <node if="$(arg rviz)" pkg="rviz" type="rviz" name="rviz" args="-d $(find planning)/config/planning.rviz"/>
+</launch>
diff --git a/planning/package.xml b/planning/package.xml
new file mode 100644
index 0000000..cd51116
--- /dev/null
+++ b/planning/package.xml
@@ -0,0 +1,47 @@
+<?xml version="1.0"?>
+<package format="3">
+ <name>planning</name>
+ <version>1.0.0</version>
+ <description>CSE 478 MuSHR Planning Project</description>
+
+ <author>Brian Hou</author>
+ <author>Gilwoo Lee</author>
+ <author>Mohit Shridhar</author>
+ <author>Nick Walker</author>
+
+ <maintainer email="mushr@cs.washington.edu">UW CSE Robotics Teaching Staff</maintainer>
+
+ <url>https://courses.cs.washington.edu/courses/cse478/</url>
+ <url>https://mushr.io/</url>
+
+ <license>Educational Use Only</license>
+
+ <test_depend>roslaunch</test_depend>
+ <buildtool_depend>catkin</buildtool_depend>
+ <build_depend>rospy</build_depend>
+ <build_depend>std_msgs</build_depend>
+ <build_export_depend>rospy</build_export_depend>
+ <build_export_depend>std_msgs</build_export_depend>
+ <exec_depend>geometry_msgs</exec_depend>
+ <exec_depend>cse478</exec_depend>
+ <exec_depend>localization</exec_depend>
+ <exec_depend>control</exec_depend>
+
+ <exec_depend condition="$ROS_PYTHON_VERSION == 2">ipython</exec_depend>
+ <exec_depend condition="$ROS_PYTHON_VERSION == 3">ipython3</exec_depend>
+ <exec_depend condition="$ROS_PYTHON_VERSION == 2">python-matplotlib</exec_depend>
+ <exec_depend condition="$ROS_PYTHON_VERSION == 3">python3-matplotlib</exec_depend>
+ <exec_depend condition="$ROS_PYTHON_VERSION == 2">python-networkx</exec_depend>
+ <exec_depend condition="$ROS_PYTHON_VERSION == 3">python3-networkx</exec_depend>
+ <exec_depend condition="$ROS_PYTHON_VERSION == 2">python-numpy</exec_depend>
+ <exec_depend condition="$ROS_PYTHON_VERSION == 3">python3-numpy</exec_depend>
+ <exec_depend>rospy</exec_depend>
+ <exec_depend>rviz</exec_depend>
+ <exec_depend>std_msgs</exec_depend>
+ <exec_depend>tf</exec_depend>
+ <exec_depend>visualization_msgs</exec_depend>
+
+ <export>
+
+ </export>
+</package>
diff --git a/planning/scripts/planner b/planning/scripts/planner
new file mode 100755
index 0000000..ad76cbb
--- /dev/null
+++ b/planning/scripts/planner
@@ -0,0 +1,15 @@
+#!/usr/bin/env python
+
+import rospy
+from planning.planner_ros import PlannerROS
+
+if __name__ == "__main__":
+ rospy.init_node("planner")
+
+ PlannerROS(
+ num_vertices=rospy.get_param("~num_vertices"),
+ connection_radius=float(rospy.get_param("~connection_radius")),
+ curvature=float(rospy.get_param("~curvature")),
+ tf_prefix=rospy.get_param("~tf_prefix", ""),
+ )
+ rospy.spin()
diff --git a/planning/scripts/roadmap b/planning/scripts/roadmap
new file mode 100755
index 0000000..d03f7b0
--- /dev/null
+++ b/planning/scripts/roadmap
@@ -0,0 +1,69 @@
+#!/usr/bin/env python
+
+"""Script for generating and visualizing roadmaps."""
+
+import argparse
+import matplotlib.pyplot as plt
+import numpy as np
+import os
+import rospy
+
+from cse478 import utils
+
+from planning.planner_ros import graph_location
+from planning.problems import R2Problem, SE2Problem
+from planning.roadmap import Roadmap
+from planning.samplers import samplers
+
+
+def main(args):
+ if args.use_ros_map:
+ permissible_region, map_info = utils.get_map("/static_map")
+ map_name = None
+ elif args.text_map:
+ permissible_region = np.loadtxt(args.text_map, dtype=bool)
+ _, fname = os.path.split(args.text_map)
+ map_name, _ = os.path.splitext(fname)
+ else:
+ permissible_region = np.ones((10, 10), dtype=bool)
+ map_name = "empty"
+ saveto = graph_location(
+ args.problem,
+ args.sampler,
+ args.num_vertices,
+ args.connection_radius,
+ args.curvature,
+ map_name,
+ )
+
+ if args.problem == "r2":
+ problem = R2Problem(permissible_region)
+ else:
+ problem = SE2Problem(permissible_region, curvature=args.curvature)
+ sampler = samplers[args.sampler](problem.extents)
+ rm = Roadmap(
+ problem,
+ sampler,
+ args.num_vertices,
+ connection_radius=args.connection_radius,
+ lazy=args.lazy,
+ saveto=saveto,
+ )
+ rm.visualize(show_edges=args.show_edges)
+
+
+if __name__ == "__main__":
+ parser = argparse.ArgumentParser(description="generate and visualize roadmaps")
+ parser.add_argument("-n", "--num-vertices", type=int, default=100)
+ parser.add_argument("-r", "--connection-radius", type=float, default=2.0)
+ parser.add_argument("-c", "--curvature", type=float, default=0.05)
+ parser.add_argument("--sampler", choices=samplers, default="halton")
+ parser.add_argument("--lazy", action="store_true")
+ parser.add_argument("--show-edges", action="store_true")
+ parser.add_argument("--problem", choices=("r2", "se2"), default="r2")
+ map_group = parser.add_mutually_exclusive_group()
+ map_group.add_argument("--use-ros-map", action="store_true")
+ map_group.add_argument("-m", "--text-map")
+ args = parser.parse_args(rospy.myargv()[1:])
+
+ main(args)
diff --git a/planning/scripts/run_search b/planning/scripts/run_search
new file mode 100755
index 0000000..31ee24a
--- /dev/null
+++ b/planning/scripts/run_search
@@ -0,0 +1,82 @@
+#!/usr/bin/env python
+
+"""Script for running A* on R2 and SE2 problems without ROS."""
+
+from __future__ import print_function
+
+import argparse
+import networkx as nx
+import numpy as np
+import time
+
+from planning import search
+from planning.problems import R2Problem, SE2Problem
+from planning.roadmap import Roadmap
+from planning.samplers import samplers
+
+
+def main(args):
+ assert len(args.start) == len(args.goal)
+ start = np.array(args.start)
+ goal = np.array(args.goal)
+ if args.problem == "se2":
+ start[2] = np.radians(start[2])
+ goal[2] = np.radians(goal[2])
+
+ permissible_region = np.loadtxt(args.text_map, dtype=bool)
+ if args.problem == "r2":
+ problem = R2Problem(permissible_region)
+ if args.problem == "se2":
+ problem = SE2Problem(permissible_region, curvature=args.curvature)
+ sampler = samplers[args.sampler](problem.extents)
+ start_time = time.time()
+ rm = Roadmap(problem, sampler, args.num_vertices, args.connection_radius, args.lazy)
+ start_id = rm.add_node(start, is_start=True)
+ goal_id = rm.add_node(goal, is_start=False)
+
+ try:
+ print("Running A*")
+ vpath, _ = search.astar(rm, start_id, goal_id)
+ end_time = time.time()
+ print("Path length:", rm.compute_path_length(vpath))
+ print("Planning time:", end_time - start_time)
+ print("Edges evaluated:", rm.edges_evaluated)
+ except nx.NetworkXNoPath as e:
+ print(e)
+ return
+
+ rm.visualize(show_edges=args.show_edges, vpath=vpath)
+
+ if args.shortcut:
+ print("Shortcutting A* path")
+ start_time = time.time()
+ vpath = search.shortcut(rm, vpath)
+ end_time = time.time()
+ print("Shortcut length:", rm.compute_path_length(vpath))
+ print("Shortcut time:", end_time - start_time)
+ rm.visualize(show_edges=args.show_edges, vpath=vpath)
+
+
+if __name__ == "__main__":
+ parser = argparse.ArgumentParser(
+ description="test (lazy) A* on R2 and SE2 problems"
+ )
+ parser.add_argument("-m", "--text-map", type=str, help="the environment to plan on")
+ parser.add_argument("-n", "--num-vertices", type=int, required=True)
+ parser.add_argument("-r", "--connection-radius", type=float, default=2.0)
+ parser.add_argument("--sampler", type=str, default="halton", choices=samplers)
+ parser.add_argument("--lazy", action="store_true")
+ parser.add_argument("--shortcut", action="store_true")
+ parser.add_argument("--show-edges", action="store_true")
+
+ subparsers = parser.add_subparsers(dest="problem")
+ r2_parser = subparsers.add_parser("r2", help="R2 options")
+ r2_parser.add_argument("-s", "--start", nargs=2, type=int, required=True)
+ r2_parser.add_argument("-g", "--goal", nargs=2, type=int, required=True)
+ se2_parser = subparsers.add_parser("se2", help="SE2 options")
+ se2_parser.add_argument("-s", "--start", nargs=3, type=int, required=True)
+ se2_parser.add_argument("-g", "--goal", nargs=3, type=int, required=True)
+ se2_parser.add_argument("-c", "--curvature", type=float, default=0.05)
+ args = parser.parse_args()
+
+ main(args)
diff --git a/planning/setup.py b/planning/setup.py
new file mode 100644
index 0000000..b39c5ab
--- /dev/null
+++ b/planning/setup.py
@@ -0,0 +1,9 @@
+# ! DO NOT MANUALLY INVOKE THIS setup.py, USE CATKIN INSTEAD
+
+from setuptools import setup
+from catkin_pkg.python_setup import generate_distutils_setup
+
+# fetch values from package.xml
+setup_args = generate_distutils_setup(packages=["planning"], package_dir={"": "src"})
+
+setup(**setup_args)
diff --git a/planning/src/planning/__init__.py b/planning/src/planning/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/planning/src/planning/dubins.py b/planning/src/planning/dubins.py
new file mode 100644
index 0000000..b1dfede
--- /dev/null
+++ b/planning/src/planning/dubins.py
@@ -0,0 +1,313 @@
+"""Dubins path code (https://en.wikipedia.org/wiki/Dubins_path), originally
+authored by Atsushi Sakai (@Atsushi_twi).
+
+Modified by Aditya Vamsikrishna (adityavk), Gilwoo Lee (gilwoo), and Nick Walker
+(nswalker) for CSE 478.
+"""
+
+import math
+
+import matplotlib.pyplot as plt
+import numpy as np
+from cse478 import utils
+
+
+def path_planning(start, end, curvature, resolution=0.1):
+ """Return the Dubins path between two states.
+
+ Args:
+ start: list of (sx, sy, stheta)
+ sx: x position of start state [m]
+ sy: y position of start state [m]
+ stheta: yaw angle of start state [rad]
+ end: list of (ex, ey, etheta)
+ ex: x position of end state [m]
+ ey: y position of end state [m]
+ etheta: yaw angle of end state [rad]
+ curvature: Dubins path curvature [1/m]
+ resolution: interpolation resolution
+
+ Returns:
+ path: interpolated Dubins path between start and end
+ length: length of Dubins path
+ """
+ # Transform to make start of path the origin
+ start = np.array(start, dtype=float)
+ end = np.array(end, dtype=float)
+ end[:2] -= start[:2]
+ start_orientation = start[2]
+ rotation = utils.rotation_matrix(start_orientation)
+ end[:2] = np.matmul(end[:2], rotation)
+ end[2] -= start[2]
+
+ path, mode, length = path_planning_from_origin(
+ end, curvature, resolution=resolution
+ )
+ # Transform back into original reference frame
+ inv_rot = utils.rotation_matrix(-start_orientation)
+ path[:, :2] = np.matmul(path[:, :2], inv_rot) + start[:2]
+ path[:, 2] += start[2]
+ pi_2_pi(path[:, 2])
+
+ # Turn this back on if you see unnecessary rotations
+ # path, length = process_dubins(start, path, length)
+
+ # FIXME(nickswalker5-17-21): The dubins stuff is littered with this
+ # "scale by curvature." It can probably be refactored out
+ real_path_length = length * 1 / curvature
+ return path, real_path_length
+
+
+def path_length(s, e, c):
+ """Return the length of the Dubins path between two states.
+
+ Args:
+ s: see documentation for start above
+ e: see documentation for end above
+ c: see documentation for curvature above
+
+ Returns:
+ length: length of Dubins path
+ """
+ # Transform to make start of path the origin
+ start = np.array(s, dtype=float)
+ end = np.array(e, dtype=float)
+ end[:2] -= start[:2]
+ start_orientation = start[2]
+ rotation = utils.rotation_matrix(start_orientation)
+ end[:2] = np.matmul(end[:2], rotation)
+ end[2] -= start[2]
+
+ lengths, mode, cost = get_best_plan_from_origin(end, c)
+ return cost * (1 / c)
+
+
+##########################
+# Implementation Details #
+##########################
+
+
+def mod2pi(theta):
+ return theta - 2.0 * math.pi * math.floor(theta / 2.0 / math.pi)
+
+
+def pi_2_pi(angles):
+ angles[:] = (angles[:] - np.pi) % (2 * np.pi) + np.pi
+
+
+def lsl(alpha, beta, d):
+ sa = math.sin(alpha)
+ sb = math.sin(beta)
+ ca = math.cos(alpha)
+ cb = math.cos(beta)
+ c_ab = math.cos(alpha - beta)
+
+ tmp0 = d + sa - sb
+
+ mode = "LSL"
+ p_squared = 2 + (d * d) - (2 * c_ab) + (2 * d * (sa - sb))
+ if p_squared < 0:
+ return None, None, None, mode
+ tmp1 = math.atan2((cb - ca), tmp0)
+ t = mod2pi(-alpha + tmp1)
+ p = math.sqrt(p_squared)
+ q = mod2pi(beta - tmp1)
+
+ return t, p, q, mode
+
+
+def rsr(alpha, beta, d):
+ sa = math.sin(alpha)
+ sb = math.sin(beta)
+ ca = math.cos(alpha)
+ cb = math.cos(beta)
+ c_ab = math.cos(alpha - beta)
+
+ tmp0 = d - sa + sb
+ mode = "RSR"
+ p_squared = 2 + (d * d) - (2 * c_ab) + (2 * d * (sb - sa))
+ if p_squared < 0:
+ return None, None, None, mode
+ tmp1 = math.atan2((ca - cb), tmp0)
+ t = mod2pi(alpha - tmp1)
+ p = math.sqrt(p_squared)
+ q = mod2pi(-beta + tmp1)
+
+ return t, p, q, mode
+
+
+def lsr(alpha, beta, d):
+ sa = math.sin(alpha)
+ sb = math.sin(beta)
+ ca = math.cos(alpha)
+ cb = math.cos(beta)
+ c_ab = math.cos(alpha - beta)
+
+ p_squared = -2 + (d * d) + (2 * c_ab) + (2 * d * (sa + sb))
+ mode = "LSR"
+ if p_squared < 0:
+ return None, None, None, mode
+ p = math.sqrt(p_squared)
+ tmp2 = math.atan2((-ca - cb), (d + sa + sb)) - math.atan2(-2.0, p)
+ t = mod2pi(-alpha + tmp2)
+ q = mod2pi(-mod2pi(beta) + tmp2)
+
+ return t, p, q, mode
+
+
+def rsl(alpha, beta, d):
+ sa = math.sin(alpha)
+ sb = math.sin(beta)
+ ca = math.cos(alpha)
+ cb = math.cos(beta)
+ c_ab = math.cos(alpha - beta)
+
+ p_squared = (d * d) - 2 + (2 * c_ab) - (2 * d * (sa + sb))
+ mode = "RSL"
+ if p_squared < 0:
+ return None, None, None, mode
+ p = math.sqrt(p_squared)
+ tmp2 = math.atan2((ca + cb), (d - sa - sb)) - math.atan2(2.0, p)
+ t = mod2pi(alpha - tmp2)
+ q = mod2pi(beta - tmp2)
+
+ return t, p, q, mode
+
+
+def rlr(alpha, beta, d):
+ sa = math.sin(alpha)
+ sb = math.sin(beta)
+ ca = math.cos(alpha)
+ cb = math.cos(beta)
+ c_ab = math.cos(alpha - beta)
+
+ mode = "RLR"
+ tmp_rlr = (6.0 - d * d + 2.0 * c_ab + 2.0 * d * (sa - sb)) / 8.0
+ if abs(tmp_rlr) > 1.0:
+ return None, None, None, mode
+
+ p = mod2pi(2 * math.pi - math.acos(tmp_rlr))
+ t = mod2pi(alpha - math.atan2(ca - cb, d - sa + sb) + mod2pi(p / 2.0))
+ q = mod2pi(alpha - beta - t + mod2pi(p))
+ return t, p, q, mode
+
+
+def lrl(alpha, beta, d):
+ sa = math.sin(alpha)
+ sb = math.sin(beta)
+ ca = math.cos(alpha)
+ cb = math.cos(beta)
+ c_ab = math.cos(alpha - beta)
+
+ mode = "LRL"
+ tmp_lrl = (6.0 - d * d + 2.0 * c_ab + 2.0 * d * (-sa + sb)) / 8.0
+ if abs(tmp_lrl) > 1:
+ return None, None, None, mode
+ p = mod2pi(2 * math.pi - math.acos(tmp_lrl))
+ t = mod2pi(-alpha - math.atan2(ca - cb, d + sa - sb) + p / 2.0)
+ q = mod2pi(mod2pi(beta) - alpha - t + mod2pi(p))
+
+ return t, p, q, mode
+
+
+def get_best_plan_from_origin(goal, curvature):
+ # nomalize
+ d = math.sqrt(goal[0] ** 2.0 + goal[1] ** 2.0)
+ d = d * curvature
+ # print(dx, dy, D, d)
+
+ theta = mod2pi(math.atan2(goal[1], goal[0]))
+ alpha = mod2pi(-theta)
+ beta = mod2pi(goal[2] - theta)
+ # print(theta, alpha, beta, d)
+
+ planners = [lsl, rsr, lsr, rsl, rlr, lrl]
+
+ bcost = float("inf")
+ bt, bp, bq, bmode = None, None, None, None
+
+ for planner in planners:
+ t, p, q, mode = planner(alpha, beta, d)
+ if t is None:
+ continue
+
+ cost = abs(t) + abs(p) + abs(q)
+ if bcost > cost:
+ bt, bp, bq, bmode = t, p, q, mode
+ bcost = cost
+
+ return [bt, bp, bq], bmode, bcost
+
+
+def path_planning_from_origin(goal, curvature, resolution=0.1):
+ if np.all(np.array(goal) == 0.0):
+ return np.zeros((1, 3)), [], 0.0
+
+ lengths, modes, cost = get_best_plan_from_origin(goal, curvature)
+
+ path = generate_course(lengths, modes, curvature, step_size=resolution)
+ return path, modes, cost
+
+
+def line(length=10.0, waypoint_sep=0.1):
+ # Desired waypoint_sep may not divide the length evenly.
+ # In that case, we'll round up (add at most one extra waypoint),
+ # but still hit the length exactly.
+ # Need at least a start and end (2 points)
+ num_waypoints = max(math.ceil(length / waypoint_sep), 2)
+ line_coords = np.mgrid[0 : length : complex(num_waypoints)]
+ return np.vstack([line_coords, np.zeros((2, len(line_coords)))]).T
+
+
+def turn(radius, angle, waypoint_sep=0.1):
+ turn_center = [0, radius]
+ arclength = angle * radius
+ # Need at least a start and end (2 points)
+ num_turn_points = max(math.ceil(arclength / waypoint_sep), 2)
+ thetas = np.mgrid[0 : angle : complex(num_turn_points)]
+ thetas -= np.pi / 2
+ turn_poses = np.empty((len(thetas), 3))
+ turn_poses[:, 0] = radius * np.cos(thetas) + turn_center[0]
+ turn_poses[:, 1] = radius * np.sin(thetas) + turn_center[1]
+ turn_poses[:, 2] = thetas + (np.pi / 2)
+ return turn_poses
+
+
+def generate_course(lengths, mode, curvature, step_size=0.1):
+ segments = []
+ for m, length in zip(mode, lengths):
+ # 0 length segments are just a noop
+ if length == 0.0:
+ continue
+ if m == "S":
+ segments.append(line(length / curvature, step_size))
+ elif m == "L":
+ segments.append(turn(1 / curvature, length, step_size))
+ elif m == "R":
+ segments.append(turn(1 / curvature, length, step_size) * [1, -1, -1])
+ else:
+ raise RuntimeError("Unexpected mode type '{}'".format(m))
+
+ # Each of the segments starts at the origin. String
+ # them together by transforming each to start
+ # at the previous' endpoint
+ for i in range(1, len(segments)):
+ seg = segments[i]
+ start = segments[i - 1][-1]
+ rot = utils.rotation_matrix(-start[2])
+ seg[:, :2] = np.matmul(seg[:, :2], rot) + start[:2]
+ seg[:, 2] += start[2]
+ return np.vstack(segments)
+
+
+def process_dubins(start, path, cost):
+ """Ensure no 2pi rotations in the output due to numerical issues."""
+ eps = 1e-6
+ close_to_start = np.sum(abs(path - start) < eps, axis=1) == 3
+ ind = np.where(close_to_start[1 : len(path) - 1])[0]
+ if len(ind) > 0:
+ return path[ind[0] + 1 :, :], cost - math.radians(360.0)
+
+ return path, cost
+
+
diff --git a/planning/src/planning/planner_ros.py b/planning/src/planning/planner_ros.py
new file mode 100644
index 0000000..3b00556
--- /dev/null
+++ b/planning/src/planning/planner_ros.py
@@ -0,0 +1,259 @@
+from __future__ import print_function
+
+import errno
+
+import networkx as nx
+import numpy as np
+import os
+import rospy
+import tf2_ros
+import time
+
+from geometry_msgs.msg import PoseStamped, PoseArray, Point
+from visualization_msgs.msg import Marker
+from nav_msgs.msg import Path
+from std_msgs.msg import Header
+
+from cse478 import utils
+from control.srv import FollowPath
+from planning import search
+from planning.problems import SE2Problem
+from planning.roadmap import Roadmap
+from planning.samplers import samplers
+
+
+class PlannerROS:
+ def __init__(
+ self,
+ num_vertices,
+ connection_radius,
+ curvature,
+ do_shortcut=True,
+ sampler_type="halton",
+ tf_prefix="",
+ tf_listener=None,
+ ):
+ """Motion planning ROS wrapper.
+
+ Args:
+ num_vertices: number of vertices in the graph
+ connection_radius: radius for connecting vertices
+ do_shortcut: whether to shortcut the planned path
+ sampler_type: name of the sampler to construct
+ """
+ self.tf_prefix = tf_prefix
+
+ if tf_listener:
+ self.tl = tf_listener
+ else:
+ self.tl = tf2_ros.TransformListener(tf2_ros.Buffer())
+
+ self.num_vertices = num_vertices
+ self.connection_radius = connection_radius
+ self.do_shortcut = do_shortcut
+
+
+ self.permissible_region, self.map_info = utils.get_map("/static_map")
+ self.problem = SE2Problem(
+ self.permissible_region, check_resolution=0.1, curvature=curvature
+ )
+ self.rm = None
+
+ self.goal_sub = rospy.Subscriber(
+ "/move_base_simple/goal", PoseStamped, self.get_goal
+ )
+
+ self.nodes_viz = rospy.Publisher(
+ "~vertices", PoseArray, queue_size=1, latch=True
+ )
+ self.edges_viz = rospy.Publisher("~edges", Marker, queue_size=1, latch=True)
+ self.controller = rospy.ServiceProxy("controller/follow_path", FollowPath)
+
+ # Load or construct a roadmap
+ saveto = graph_location(
+ "se2", sampler_type, num_vertices, connection_radius, curvature
+ )
+ sampler = samplers[sampler_type](self.problem.extents)
+ rospy.loginfo("Constructing roadmap...")
+ rospy.loginfo(saveto)
+ start_stamp = time.time()
+ self.rm = Roadmap(
+ self.problem,
+ sampler,
+ num_vertices,
+ connection_radius,
+ lazy=True,
+ saveto=saveto,
+ )
+ load_time = time.time() - start_stamp
+ rospy.loginfo("Roadmap constructed in {:2.2f}s".format(load_time))
+ self.visualize()
+ # self.rm.visualize(saveto="graph.png")
+
+ def plan_to_goal(self, start, goal):
+ """Return a planned path from start to goal."""
+ # Add current pose and goal to the planning env
+ rospy.loginfo("Adding start and goal node")
+ try:
+ start_id = self.rm.add_node(start, is_start=True)
+ goal_id = self.rm.add_node(goal, is_start=False)
+ except ValueError:
+ rospy.loginfo("Either start or goal was in collision")
+ return None
+ # self.rm.visualize(saveto="graph.png")
+
+ # Call the Lazy A* planner
+ try:
+ rospy.loginfo("Planning...")
+ start_time = time.time()
+ path, _ = search.astar(self.rm, start_id, goal_id)
+ end_time = time.time()
+ rospy.loginfo("Path length: {}".format(self.rm.compute_path_length(path)))
+ rospy.loginfo("Planning time: {}".format(end_time - start_time))
+ rospy.loginfo("Edges evaluated: {}".format(self.rm.edges_evaluated))
+ # self.rm.visualize(vpath=path, saveto="planned_path.png")
+ except nx.NetworkXNoPath:
+ rospy.loginfo("Failed to find a plan")
+ return None
+
+ # Shortcut if necessary
+ if self.do_shortcut:
+ rospy.loginfo("Shortcutting path...")
+ start_time = time.time()
+ path = search.shortcut(self.rm, path)
+ end_time = time.time()
+ rospy.loginfo(
+ "Shortcut length: {}".format(self.rm.compute_path_length(path))
+ )
+ rospy.loginfo("Shortcut time: {}".format(end_time - start_time))
+ # self.rm.visualize(vpath=path, saveto="shortcut_path.png")
+
+ states = []
+ for i in range(1, len(path)):
+ u, v = path[i - 1 : i + 1]
+ q1 = self.rm.vertices[u, :]
+ q2 = self.rm.vertices[v, :]
+ edge, _ = self.rm.problem.steer(q1, q2)
+ states.append(edge)
+ states = np.vstack(states)
+ return utils.map_to_world(states, self.map_info)
+
+
+ def get_goal(self, msg):
+ """Goal callback function."""
+ if self.rm is None:
+ return False
+
+ self.goal = np.array([utils.pose_to_particle(msg.pose)])
+ utils.world_to_map(self.goal, self.map_info)
+ self.goal = self.goal.squeeze()
+
+ start = self._get_car_pose()[np.newaxis, :]
+ utils.world_to_map(start, self.map_info)
+ start = start.squeeze()
+
+ path_states = self.plan_to_goal(start, self.goal)
+ if path_states is None:
+ return False
+ return self.send_path(path_states)
+
+ def _get_car_pose(self):
+ """Return the current vehicle state."""
+ try:
+ transform = self.tl.buffer.lookup_transform(
+ "map", self.tf_prefix + "base_footprint", rospy.Time(0)
+ )
+ # Drop stamp header
+ transform = transform.transform
+ return np.array(
+ [
+ transform.translation.x,
+ transform.translation.y,
+ utils.quaternion_to_angle(transform.rotation),
+ ]
+ )
+ except (
+ tf2_ros.LookupException,
+ tf2_ros.ConnectivityException,
+ tf2_ros.ExtrapolationException,
+ ) as e:
+ rospy.logwarn_throttle(5, e)
+ return None
+
+ def send_path(self, path_states):
+ """Send a planned path to the controller."""
+ h = Header()
+ h.stamp = rospy.Time.now()
+ h.frame_id = "map"
+ desired_speed = 1.0
+
+ path = Path()
+ path.header = h
+ path.poses = [
+ PoseStamped(h, utils.particle_to_pose(state)) for state in path_states
+ ]
+
+ return self.controller(path, desired_speed)
+
+ def visualize(self):
+ """Visualize the nodes and edges of the roadmap."""
+ vertices = self.rm.vertices.copy()
+ utils.map_to_world(vertices, self.map_info)
+ poses = list(map(utils.particle_to_pose, vertices))
+ msg = PoseArray(header=Header(frame_id="map"), poses=poses)
+ self.nodes_viz.publish(msg)
+ return
+
+ all_edges = []
+ edges = list(self.rm.graph.edges())
+ for u, v in edges:
+ if not self.rm.check_edge_validity(u, v):
+ continue
+ q1 = self.rm.vertices[u, :]
+ q2 = self.rm.vertices[v, :]
+ edge, _ = self.problem.steer(q1, q2)
+ utils.map_to_world(edge, self.map_info)
+ with_repeats = np.repeat(edge[:, :2], 2, 0).reshape(-1, 2)[1:-1]
+ all_edges.append(with_repeats)
+ all_edges = np.vstack(all_edges)
+ points = list(map(lambda x: Point(x=x[0], y=x[1]), all_edges))
+ msg = Marker(
+ header=Header(frame_id="map"), type=Marker.LINE_LIST, points=points
+ )
+ msg.scale.x = 0.01
+ msg.pose.orientation.w = 1.0
+ msg.color.a = 0.1
+ self.edges_viz.publish(msg)
+
+
+def mkdir_p(path):
+ """Equivalent to mkdir -p path."""
+ try:
+ os.makedirs(path)
+ except OSError as exc: # Python ≥ 2.5
+ if exc.errno == errno.EEXIST and os.path.isdir(path):
+ pass
+ # possibly handle other errno cases here, otherwise finally:
+ else:
+ raise
+
+
+def graph_location(
+ problem_name,
+ sampler_name,
+ num_vertices,
+ connection_radius,
+ curvature=None,
+ map_name=None,
+):
+ """Return the name of this graph in the cache."""
+ param_string = "_{}_{}_{}_{}".format(
+ problem_name, sampler_name, num_vertices, connection_radius
+ )
+ if problem_name == "se2":
+ param_string += "_{}".format(curvature)
+ cache_dir = os.path.expanduser("~/.ros/graph_cache/")
+ mkdir_p(cache_dir)
+ if map_name is None:
+ map_name = os.path.basename(rospy.get_param("/map_file")).split(".")[0]
+ return os.path.join(cache_dir, map_name + param_string + ".pkl")
diff --git a/planning/src/planning/problems.py b/planning/src/planning/problems.py
new file mode 100644
index 0000000..cfe0a13
--- /dev/null
+++ b/planning/src/planning/problems.py
@@ -0,0 +1,183 @@
+import numpy as np
+
+from planning import dubins
+
+
+class PlanarProblem(object):
+ def __init__(self, permissible_region, check_resolution=0.1):
+ """Construct a planar planning problem.
+
+ Args:
+ permissible_region: Boolean np.array with shape map height x map width,
+ where one indicates that the location is permissible
+ check_resolution: collision-checking resolution
+ """
+ self.permissible_region = permissible_region
+ self.check_resolution = check_resolution
+
+ self.height, self.width = permissible_region.shape
+ self.extents = np.zeros((2, 2))
+ self.extents[0, 1] = self.width
+ self.extents[1, 1] = self.height
+
+ def check_state_validity(self, states):
+ """Return whether states are valid.
+
+ Valid states are within the extents of the map and collision-free.
+
+ Args:
+ states: np.array with shape N x D (where D may be 2 or 3)
+
+ Returns:
+ valid: np.array of Booleans with shape N
+ """
+ x = states[:, 0]
+ y = states[:, 1]
+ valid = np.ones_like(x, dtype=bool) # feel free to delete this line
+
+ # Check that x and y are within the extents of the map.
+ # BEGIN QUESTION 1.2
+ "*** REPLACE THIS LINE ***"
+ # END QUESTION 1.2
+
+ # For states within the extents of the map, collision check by reading
+ # the corresponding entry of self.permissible_region. For simplicity,
+ # we'll assume the robot is a point robot: just index directly with the
+ # robot state x and y indices into self.permissible_region.
+ #
+ # Hint: use the `astype` method to cast the x and y positions into
+ # integers. Then, index into self.permissible_region, remembering that
+ # the zeroth dimension is the height.
+ # BEGIN QUESTION 1.2
+ "*** REPLACE THIS LINE ***"
+ # END QUESTION 1.2
+
+ return valid
+
+ def check_edge_validity(self, q1, q2):
+ """Return whether an edge is valid.
+
+ Args:
+ q1, q2: np.arrays with shape D (where D may be 2 or 3)
+
+ Returns:
+ valid: True or False
+ """
+ path, length = self.steer(q1, q2)
+ if length == 0:
+ return False
+ return self.check_state_validity(path).all()
+
+ def compute_heuristic(self, q1, q2):
+ """Compute an admissible heuristic between two states.
+
+ Args:
+ q1, q2: np.arrays with shape (N, D) (where D may be 2 or 3)
+
+ Returns:
+ heuristic: np.array with shape N of cost estimates between pairs of states
+ """
+ # Subclasses can override this with more efficient implementations.
+ start, end = np.atleast_2d(q1), np.atleast_2d(q2)
+ start_ind = np.arange(start.shape[0])
+ end_ind = np.arange(end.shape[0])
+ # We'll use broadcasting semantics to match up
+ # potentially differently shaped inputs
+ broad = np.broadcast(start_ind, end_ind)
+ num_pairs = broad.size
+ heuristic_cost = np.empty((num_pairs))
+ for i, (start_i, end_i) in enumerate(zip(*broad.iters)):
+ _, length = self.steer(start[start_i], end[end_i])
+ heuristic_cost[i] = length
+ return heuristic_cost
+
+ def steer(self, q1, q2):
+ """Return a local path connecting two states.
+
+ Intermediate states are used for edge collision-checking.
+
+ Args:
+ q1, q2: np.arrays with shape D (where D may be 2 or 3)
+
+ Returns:
+ path: sequence of states between q1 and q2
+ length: length of local path
+ """
+ raise NotImplementedError
+
+
+class R2Problem(PlanarProblem):
+ def compute_heuristic(self, q1, q2):
+ """Compute the Euclidean distance between two states.
+
+ Args:
+ q1, q2: np.arrays with shape (N, 2)
+
+ Returns:
+ heuristic: cost estimate between two states
+ """
+ return np.linalg.norm(np.atleast_2d(q2) - np.atleast_2d(q1), axis=1)
+
+ def steer(self, q1, q2):
+ """Return a straight-line path connecting two R^2 states.
+
+ Args:
+ q1, q2: np.arrays with shape 2
+
+ Returns:
+ path: sequence of states between q1 and q2
+ length: length of local path
+ """
+ q1 = q1.reshape((1, -1))
+ q2 = q2.reshape((1, -1))
+ dist = np.linalg.norm(q2 - q1)
+ if dist < self.check_resolution:
+ return np.vstack((q1, q2)), dist
+ q1_toward_q2 = (q2 - q1) / dist
+ steps = np.hstack(
+ (np.arange(0, dist, self.check_resolution), np.array([dist]))
+ ).reshape((-1, 1))
+ return q1 + q1_toward_q2 * steps, dist
+
+
+class SE2Problem(PlanarProblem):
+ def __init__(self, permissible_region, check_resolution=0.01, curvature=3):
+ super(SE2Problem, self).__init__(permissible_region, check_resolution)
+ self.curvature = curvature
+ self.extents = np.vstack((self.extents, np.array([[-np.pi, np.pi]])))
+
+ def compute_heuristic(self, q1, q2):
+ """Compute the length of the Dubins path between two SE(2) states.
+
+ Args:
+ q1, q2: np.arrays with shape 3
+
+ Returns:
+ heuristic: cost estimate between two states
+ """
+ start, end = np.atleast_2d(q1), np.atleast_2d(q2)
+ start_ind = np.arange(start.shape[0])
+ end_ind = np.arange(end.shape[0])
+ broad = np.broadcast(start_ind, end_ind)
+ num_pairs = broad.size
+ heuristic_cost = np.empty((num_pairs))
+ for i, (start_i, end_i) in enumerate(zip(*broad.iters)):
+ heuristic_cost[i] = dubins.path_length(
+ start[start_i], end[end_i], self.curvature
+ )
+ return heuristic_cost
+
+ def steer(self, q1, q2):
+ """Return a Dubins path connecting two SE(2) states.
+
+ Args:
+ q1, q2: np.arrays with shape 3
+
+ Returns:
+ path: sequence of states on Dubins path between q1 and q2
+ length: length of local path
+ """
+ path, length = dubins.path_planning(
+ q1, q2, self.curvature, resolution=self.check_resolution
+ )
+ return path, length
diff --git a/planning/src/planning/roadmap.py b/planning/src/planning/roadmap.py
new file mode 100644
index 0000000..2bf45de
--- /dev/null
+++ b/planning/src/planning/roadmap.py
@@ -0,0 +1,357 @@
+from __future__ import division
+
+import os
+import networkx as nx
+import numpy as np
+import matplotlib.pyplot as plt
+import pickle
+
+from planning import problems
+from planning import samplers
+
+
+class Roadmap(object):
+ def __init__(
+ self, problem, sampler, num_vertices, connection_radius, lazy=False, saveto=None
+ ):
+ """Construct a motion planning roadmap.
+
+ Nodes in the roadmap are labeled with unique integers. These labels
+ are indices into the graph's vertices.
+
+ Args:
+ problem: a problem description (either an R2Problem or SE2Problem)
+ sampler: a sampler (either a HaltonSampler, LatticeSampler, RandomSampler)
+ num_vertices: desired number of vertices in the roadmap
+ connection_radius: connection radius between vertices
+ lazy: whether the roadmap edges should be lazily collision-checked
+ saveto: path to cached roadmap data
+
+ """
+ self.problem = problem
+ self.sampler = sampler
+ self.num_vertices = num_vertices
+ self.connection_radius = connection_radius
+ self.lazy = lazy
+ self.saveto = saveto
+
+ # R2 graph is undirected, SE3 graph is directed
+ self.directed = isinstance(self.problem, problems.SE2Problem)
+
+ self.start = None
+ self.goal = None
+ self.edges_evaluated = 0
+ self.graph, self.vertices, self.weighted_edges = self.construct()
+
+ # Print some graph summary statistics
+ print("Lazy:", self.lazy)
+ print("Vertices:", self.num_vertices)
+ print("Edges:", self.weighted_edges.shape[0])
+
+ def heuristic(self, n1, n2):
+ """Compute the heuristic between two nodes in the roadmap.
+
+ Args:
+ n1, n2 (int): node labels
+
+ Returns:
+ heuristic: cost estimate between two nodes
+ """
+ return self.problem.compute_heuristic(
+ self.vertices[n1, :],
+ self.vertices[n2, :],
+ )
+
+ def check_edge_validity(self, n1, n2):
+ """Collision check the edge between two nodes in the roadmap.
+
+ Args:
+ n1, n2 (int): node labels
+
+ Returns:
+ valid: whether the edge is collision-free
+ """
+ self.edges_evaluated += 1
+ return self.problem.check_edge_validity(
+ self.vertices[n1, :],
+ self.vertices[n2, :],
+ )
+
+ def check_weighted_edges_validity(self, weighted_edges):
+ """Collision check the edges in weighted_edges.
+
+ Args:
+ weighted_edges: np.array of edges and edge lengths with shape
+ num_edges x 3, where each row is (u, v, length) and u, v are
+ node labels
+
+ Returns:
+ weighted_edges: a subset of the original weighted_edges, where only
+ rows that correspond to collision-free edges are preserved
+ """
+ # uv contains only the node labels for each edge (source, target).
+ uv = weighted_edges[:, :2].astype(int)
+
+ # If edges aren't lazily evaluated, collision-check all the edges and
+ # only keep collision-free edges in weighted_edges.
+ #
+ # Your implementation should call the check_edge_validity method above.
+ # BEGIN QUESTION 1.3
+ "*** REPLACE THIS LINE ***"
+ raise NotImplementedError
+ # END QUESTION 1.3
+ return weighted_edges
+
+ def construct(self):
+ """Construct the roadmap.
+
+ Initialize the graph, vertices, and weighted_edges fields.
+
+ Returns:
+ graph: a NetworkX graph
+ vertices: np.array of states with shape num_vertices x D,
+ indexed by node labels
+ weighted_edges: np.array of edges and edge lengths with shape
+ num_edges x 3, where each row is (u, v, length) and u, v are
+ node labels
+ """
+ # Load a cached roadmap, if one exists at that path.
+ if self.saveto is not None and os.path.exists(self.saveto):
+ try:
+ with open(self.saveto, "rb") as f:
+ data = pickle.load(f)
+ self.graph = data["graph"]
+ self.vertices = data["vertices"]
+ self.weighted_edges = data["weighted_edges"]
+ print("Loaded roadmap from", self.saveto)
+ return self.graph, self.vertices, self.weighted_edges
+ except pickle.PickleError:
+ pass
+
+ # Compute the set of vertices and edges.
+ self.vertices = self.sample_vertices()
+ self.weighted_edges = self.connect_vertices(self.vertices)
+ if not self.lazy:
+ self.weighted_edges = self.check_weighted_edges_validity(
+ self.weighted_edges
+ )
+
+ # Insert the vertices and edges into a NetworkX graph object
+ if self.directed:
+ self.graph = nx.DiGraph()
+ else:
+ self.graph = nx.Graph()
+ vbunch = [
+ (i, dict(config=config))
+ for i, config in zip(np.arange(self.num_vertices, dtype=int), self.vertices)
+ ]
+ ebunch = [(int(u), int(v), float(w)) for u, v, w in self.weighted_edges]
+ self.graph.add_nodes_from(vbunch)
+ self.graph.add_weighted_edges_from(ebunch, "weight")
+
+ # Cache this roadmap, if path is specified.
+ if self.saveto is not None:
+ with open(self.saveto, "wb") as f:
+ data = {
+ "graph": self.graph,
+ "vertices": self.vertices,
+ "weighted_edges": self.weighted_edges,
+ }
+ pickle.dump(data, f)
+ print("Saved roadmap to", self.saveto)
+ return self.graph, self.vertices, self.weighted_edges
+
+ def sample_vertices(self):
+ """Sample self.num_vertices vertices from self.sampler.
+
+ Returns:
+ vertices: np.array of states with shape num_vertices x D
+ """
+ # Lattice sampler only samples a fixed set of samples. Some of them may
+ # be in collision, so they're unusable; pretend we only asked for that
+ # many vertices.
+ if isinstance(self.sampler, samplers.LatticeSampler):
+ samples = self.sampler.sample(self.num_vertices)
+ valid = self.problem.check_state_validity(samples)
+ self.num_vertices = valid.sum()
+ return samples[valid, :]
+
+ # For other samplers, sample until the target number of collision-free
+ # vertices is reached.
+ configs = []
+ batch_size = self.num_vertices
+ valid_samples = 0
+ total_samples = 0
+ while valid_samples < self.num_vertices:
+ samples = self.sampler.sample(batch_size)
+ valid = self.problem.check_state_validity(samples)
+ configs.append(samples[valid, :])
+ valid_samples += valid.sum()
+ total_samples += batch_size
+ # This adaptively changes the sample batch size based on how hard it
+ # is to get collision-free vertices.
+ est_validity = valid_samples / total_samples
+ batch_size = int((self.num_vertices - valid_samples) / est_validity) + 1
+ configs = np.vstack(configs)
+ assert configs.shape[0] >= self.num_vertices
+ return configs[: self.num_vertices, :]
+
+ def connect_vertices(self, vertices):
+ """Connect vertices within self.connection_radius.
+
+ Returns:
+ weighted_edges: np.array of edges and edge lengths with shape
+ num_edges x 3, where each row is (u, v, length) and u, v are
+ node labels (vertex indices)
+ """
+ h = np.zeros((self.num_vertices, self.num_vertices))
+ for i in range(self.num_vertices):
+ u = vertices[i, :]
+ # For undirected graphs, only compute heuristic to all later vertices.
+ # For directed graphs, compute heuristic to previous vertices as well.
+ h[i, i + 1 :] = self.problem.compute_heuristic(u, vertices[i + 1 :, :])
+
+ if self.directed:
+ h[i, :i] = self.problem.compute_heuristic(u, vertices[:i, :])
+
+ # Edges where the heuristic distance is greater than the connection
+ # radius are dropped. Find the indices to all nonzero heuristics; i is
+ # the sources and j is the targets.
+ h[h > self.connection_radius] = 0
+ i, j = h.nonzero()
+ i = i.reshape((-1, 1))
+ j = j.reshape((-1, 1))
+ return np.hstack([i, j, h[i, j]])
+
+ def add_node(self, state, is_start):
+ """Add a node for the state, which is either a start or goal.
+
+ Args:
+ state: new state to add to the roadmap
+ is_start: whether this new state is the start
+
+ Returns:
+ new_index: the integer label for the added state
+ """
+ assert len(state.shape) == 1
+ valid = self.problem.check_state_validity(state.reshape((1, -1))).item()
+ if not valid:
+ raise ValueError("state {} is invalid".format(state))
+
+ index = self.graph.number_of_nodes()
+
+ # This logic is very similar to connect_vertices. Either compute the
+ # heuristic from the start to all other vertices, or from all other
+ # vertices to the goal.
+ if is_start:
+ self.start = index
+ h = self.problem.compute_heuristic(state, self.vertices)
+
+ else:
+ self.goal = index
+ h = self.problem.compute_heuristic(self.vertices, state)
+ h[h > self.connection_radius] = 0
+ (i,) = h.nonzero()
+ i = i.reshape((-1, 1))
+ index_arr = index * np.ones_like(i)
+ if is_start:
+ weighted_edges = np.hstack([index_arr, i, h[i]])
+ else:
+ weighted_edges = np.hstack([i, index_arr, h[i]])
+
+ # Update self.graph, self.vertices, and self.weighted_edges.
+ self.graph.add_node(index, config=state)
+ self.vertices = np.vstack([self.vertices, state.reshape((1, -1))])
+ self.num_vertices += 1
+ if not self.lazy:
+ weighted_edges = self.check_weighted_edges_validity(weighted_edges)
+ ebunch = [(int(u), int(v), float(w)) for u, v, w in weighted_edges]
+ self.graph.add_weighted_edges_from(ebunch)
+ self.weighted_edges = np.vstack([self.weighted_edges, weighted_edges])
+ return index
+
+ def compute_path_length(self, vpath):
+ """Compute the path length of a sequence of vertices.
+
+ Args:
+ vpath: sequence of vertex labels
+
+ Returns:
+ length: path length
+ """
+ total = 0
+ for i in range(1, len(vpath)):
+ u, v = vpath[i - 1 : i + 1]
+ total += self.heuristic(u, v)
+ return total
+
+ def compute_qpath(self, vpath):
+ """Compute a sequence of states from a sequence of vertices.
+
+ Args:
+ vpath: sequence of vertex labels
+
+ Returns:
+ qpath: sequence of configuration states
+ """
+ edges = []
+ for i in range(1, len(vpath)):
+ u, v = vpath[i - 1 : i + 1]
+ q1 = self.vertices[u, :]
+ q2 = self.vertices[v, :]
+ edge, _ = self.problem.steer(q1, q2)
+ edges.append(edge)
+ return np.vstack(edges)
+
+ def visualize(self, show_edges=False, vpath=None, saveto=None):
+ """Visualize the roadmap.
+
+ Args:
+ show_edges: whether the roadmap's edges should be shown
+ vpath: sequence of vertex labels (or None)
+ saveto: path to save roadmap plot (or None)
+ """
+ plt.imshow(
+ self.problem.permissible_region,
+ cmap=plt.cm.gray,
+ aspect="equal",
+ interpolation="none",
+ vmin=0,
+ vmax=1,
+ origin="lower",
+ extent=self.problem.extents.ravel()[:4],
+ )
+
+ if show_edges:
+ for u, v in self.graph.edges():
+ q1 = self.vertices[u, :]
+ q2 = self.vertices[v, :]
+ edge, _ = self.problem.steer(q1, q2)
+ plt.plot(edge[:, 0], edge[:, 1], c="#dddddd", zorder=1)
+
+ if vpath is not None:
+ qpath = self.compute_qpath(vpath)
+ plt.plot(qpath[:, 0], qpath[:, 1], c="#0000ff", zorder=1)
+
+ plt.scatter(self.vertices[:, 0], self.vertices[:, 1], c="k", zorder=2)
+ if self.start is not None:
+ plt.scatter(
+ self.vertices[self.start, 0],
+ self.vertices[self.start, 1],
+ c="g",
+ zorder=3,
+ )
+ if self.goal is not None:
+ plt.scatter(
+ self.vertices[self.goal, 0],
+ self.vertices[self.goal, 1],
+ c="r",
+ zorder=3,
+ )
+ plt.xlim((0, self.problem.extents[0, 1]))
+ plt.ylim((0, self.problem.extents[1, 1]))
+
+ if saveto is not None:
+ plt.savefig(saveto, bbox_inches="tight")
+ print("Saved graph image to", self.saveto)
+ plt.show()
diff --git a/planning/src/planning/samplers.py b/planning/src/planning/samplers.py
new file mode 100644
index 0000000..6751b47
--- /dev/null
+++ b/planning/src/planning/samplers.py
@@ -0,0 +1,146 @@
+from __future__ import division
+
+import numpy as np
+
+try:
+ # Python 2 compatibility
+ from itertools import izip as zip
+except ImportError:
+ pass
+
+
+class Sampler(object):
+ def __init__(self, extents):
+ """Construct a sampler for the half-open interval defined by extents.
+
+ Args:
+ extents: np.array of lower and upper bounds with shape D x 2
+ """
+ self.extents = extents
+ self.dim = extents.shape[0]
+
+ def sample(self, num_samples):
+ """Return samples from the sampler.
+
+ Args:
+ num_samples: number of samples to return
+
+ Returns:
+ samples: np.array of N x D sample configurations
+ """
+ raise NotImplementedError
+
+
+class HaltonSampler(Sampler):
+ # This is hard-coded to avoid writing code to generate primes. Halton
+ # sequences with larger primes need to drop more initial terms.
+ primes = [2, 3, 5, 7, 11, 13]
+
+ def __init__(self, extents):
+ super(HaltonSampler, self).__init__(extents)
+ self.index = self.primes[self.dim] # drop the first few terms
+ self.gen = self.make_generator()
+
+ def compute_sample(self, index, base):
+ """Return an element from the Halton sequence for a desired base.
+
+ This reference may be useful: https://observablehq.com/@jrus/halton.
+
+ Args:
+ index: index of the desired element of the Halton sequence
+ base: base for the Halton sequence
+
+ Returns:
+ the element at index in the base Halton sequence
+ """
+ # This differs by one from the reference implementation. This excludes 0
+ # from our zero-indexed Halton sequence.
+ index += 1
+
+ # BEGIN QUESTION 1.1
+ "*** REPLACE THIS LINE ***"
+ raise NotImplementedError
+ # END QUESTION 1.1
+
+ def make_base_generator(self, base):
+ """Generate the Halton sequence for a desired base."""
+ while True:
+ yield self.compute_sample(self.index, base)
+
+ def make_generator(self):
+ """Generate the Halton sequence for a list of coprime bases."""
+ seqs = [self.make_base_generator(p) for p in self.primes[: self.dim]]
+ for x in zip(*seqs):
+ yield np.array(x)
+ self.index += 1
+
+ def sample(self, num_samples):
+ """Return samples from the Halton quasirandom sampler.
+
+ Args:
+ num_samples: number of samples to return
+
+ Returns:
+ samples: np.array of N x D sample configurations
+ """
+ # Generate Halton samples. Each entry lies in the range (0, 1).
+ batch = np.empty((num_samples, self.dim))
+ for i, x in zip(range(num_samples), self.gen):
+ batch[i, :] = x
+
+ # Scale the batch of samples to fit the extents of the space.
+ # BEGIN QUESTION 1.1
+ "*** REPLACE THIS LINE ***"
+ raise NotImplementedError
+ # END QUESTION 1.1
+
+
+class LatticeSampler(Sampler):
+ def __init__(self, extents):
+ super(LatticeSampler, self).__init__(extents)
+
+ def sample(self, num_samples):
+ """Return samples from the lattice sampler.
+
+ Note: this method may return fewer samples than desired.
+
+ Args:
+ num_samples: number of samples to return
+
+ Returns:
+ samples: np.array of N x D sample configurations
+ """
+ volume = np.prod(self.extents[:, 1] - self.extents[:, 0])
+ resolution = (volume / num_samples) ** (1.0 / self.dim)
+ steps = [
+ np.arange(
+ self.extents[i, 0] + resolution / 2, self.extents[i, 1], resolution
+ )
+ for i in range(self.dim)
+ ]
+ meshed = np.array(np.meshgrid(*steps)).reshape((2, -1)).T
+ return meshed[:num_samples, :]
+
+
+class RandomSampler(Sampler):
+ def sample(self, num_samples):
+ """Return samples from the random sampler.
+
+ Args:
+ num_samples: number of samples to return
+
+ Returns:
+ samples: np.array of N x D sample configurations
+ """
+ return np.random.uniform(
+ self.extents[:, 0],
+ self.extents[:, 1],
+ size=(num_samples, self.dim),
+ )
+
+
+samplers = {
+ "halton": HaltonSampler,
+ "lattice": LatticeSampler,
+ "random": RandomSampler,
+}
diff --git a/planning/src/planning/search.py b/planning/src/planning/search.py
new file mode 100644
index 0000000..0640b0e
--- /dev/null
+++ b/planning/src/planning/search.py
@@ -0,0 +1,146 @@
+"""Implementation of (Lazy) A* and shortcutting."""
+
+import collections
+import numpy as np
+import networkx as nx
+
+from itertools import count
+from cse478.utils import PriorityQueue
+
+
+# Entries in the PriorityQueue are prioritized in lexicographic order, i.e. by
+# the first element of QueueEntry (the f-value). Ties are broken by proceeding
+# to the next element, a unique counter. This prevents the underlying
+# PriorityQueue from breaking ties by comparing nodes. QueueEntry objects
+# contain the node, a possible parent, and cost-to-come via that parent. These
+# can be accessed via dot notation, e.g. `entry.node`.
+QueueEntry = collections.namedtuple(
+ "QueueEntry",
+ ["f_value", "counter", "node", "parent", "cost_to_come"],
+)
+
+NULL = -1
+
+
+def astar(rm, start, goal):
+ """Compute the shortest path from start to goal on a roadmap.
+
+ Args:
+ rm: Roadmap instance to plan on
+ start, goal: integer labels for the start and goal states
+
+ Returns:
+ vpath: a sequence of node labels, including the start and goal
+ """
+ if start not in rm.graph or goal not in rm.graph:
+ msg = "Either start {} or goal {} is not in G"
+ raise nx.NodeNotFound(msg.format(start, goal))
+
+ # expanded is a Boolean array that tracks whether each node has previously
+ # been expanded, in order to avoid expanding nodes multiple times.
+ expanded = np.zeros(rm.num_vertices, dtype=bool)
+
+ # parents is an int array that tracks the best parent for each node. It
+ # defaults to NULL (-1) for each node.
+ parents = NULL * np.ones(rm.num_vertices, dtype=int)
+
+ # heuristic_cache is a float array that caches the returned heuristic value.
+ heuristic_cache = NULL * np.ones(rm.num_vertices)
+
+ c = count()
+ queue = PriorityQueue()
+ queue.push(QueueEntry(rm.heuristic(start, goal), next(c), start, NULL, 0))
+
+ while len(queue) > 0:
+ entry = queue.pop()
+ if expanded[entry.node]:
+ continue
+
+ if rm.lazy:
+ # Collision check the edge between the parent and this node (if a
+ # parent exists). If it's in collision, stop processing this entry;
+ # we'll wait for another possible parent later in the queue.
+ # BEGIN QUESTION 2.2
+ "*** REPLACE THIS LINE ***"
+ raise NotImplementedError
+ # END QUESTION 2.2
+
+ expanded[entry.node] = True
+ parents[entry.node] = entry.parent
+
+ if entry.node == goal:
+ path = extract_path(parents, goal)
+ return path, parents
+
+ for neighbor, w in rm.graph[entry.node].items():
+ # Get the edge weight (length) and cache the heuristic value
+ weight = w.get("weight", 1)
+ if heuristic_cache[neighbor] == NULL:
+ heuristic_cache[neighbor] = rm.heuristic(neighbor, goal)
+ h = heuristic_cache[neighbor]
+
+ # Compute this neighbor's cost-to-come via entry.node, and insert a
+ # new QueueEntry.
+ #
+ # Since this may be Lazy A*, it is misleading to compare the f-value
+ # against any current QueueEntry objects for this neighbor that are
+ # already in the queue. Those may turn out to be in collision and
+ # therefore unusable. Therefore, this entry should be inserted even
+ # if it seems more costly than other entries already in the queue.
+ #
+ # However, if the neighbor has already been expanded, it's no longer
+ # necessary to insert this QueueEntry.
+ # BEGIN QUESTION 2.1
+ "*** REPLACE THIS LINE ***"
+ raise NotImplementedError
+ # END QUESTION 2.1
+ raise nx.NetworkXNoPath("Node {} not reachable from {}".format(goal, start))
+
+
+def extract_path(parents, goal):
+ """Extract the shortest path from start to goal.
+
+ Args:
+ parents: np.array of integer node labels
+ goal: integer node label for the goal state
+
+ Returns:
+ vpath: a sequence of node labels from the start to the goal
+ """
+ # Follow the parents of the node until a NULL entry is reached
+ # BEGIN QUESTION 2.1
+ "*** REPLACE THIS LINE ***"
+ raise NotImplementedError
+ # END QUESTION 2.1
+
+
+def shortcut(rm, vpath, num_trials=100):
+ """Shortcut the path between the start and goal.
+
+ Args:
+ rm: Roadmap instance to plan on
+ vpath: a sequence of node labels from the start to the goal
+ num_trials: number of random trials to attempt
+
+ Returns:
+ vpath: a subset of the original vpath that connects vertices directly
+ """
+ for _ in range(num_trials):
+ if len(vpath) == 2:
+ break
+
+ # Randomly select two indices to try and connect. Verify that an edge
+ # connecting the two vertices would be collision-free, and that
+ # connecting the two indices would actually be shorter.
+ #
+ # You may find these Roadmap methods useful: check_edge_validity,
+ # heuristic, and compute_path_length.
+ indices = np.random.choice(len(vpath), size=2, replace=False)
+ i, j = np.sort(indices)
+ # BEGIN QUESTION 2.3
+ "*** REPLACE THIS LINE ***"
+ raise NotImplementedError
+ # END QUESTION 2.3
+ return vpath
+
+
diff --git a/planning/test/astar.py b/planning/test/astar.py
new file mode 100644
index 0000000..9e6e7cc
--- /dev/null
+++ b/planning/test/astar.py
@@ -0,0 +1,184 @@
+#!/usr/bin/env python
+
+import numpy as np
+import os
+import rosunit
+import unittest
+
+from planning import search
+from planning.samplers import HaltonSampler
+from planning.problems import R2Problem, SE2Problem
+from planning.roadmap import Roadmap
+
+
+class TestAStarR2(unittest.TestCase):
+ def setUp(self):
+ fname = os.path.join(os.path.dirname(__file__), "share", "map1.txt")
+ permissible_region = np.loadtxt(fname, dtype=bool)
+ self.problem = R2Problem(permissible_region, check_resolution=0.5)
+ self.sampler = HaltonSampler(self.problem.extents)
+
+ def test_astar_r2problem(self):
+ num_vertices = 10
+ connection_radius = 5.0
+ lazy = False
+ rm = Roadmap(self.problem, self.sampler, num_vertices, connection_radius, lazy)
+
+ start_id = rm.add_node(np.array([1, 1]), is_start=True)
+ goal_id = rm.add_node(np.array([8, 8]), is_start=False)
+ correct_path = [start_id, 9, 2, 7, goal_id]
+
+ path, parents = search.astar(rm, start_id, goal_id)
+ self.assertEqual(
+ path[0],
+ start_id,
+ msg="Path must begin at start",
+ )
+ self.assertEqual(
+ path[-1],
+ goal_id,
+ msg="Path must end at goal",
+ )
+ np.testing.assert_almost_equal(
+ rm.compute_path_length(path),
+ rm.compute_path_length(correct_path),
+ err_msg="A* implementation is incorrect",
+ )
+ self.assertEqual(
+ path,
+ correct_path,
+ msg="A* implementation is incorrect",
+ )
+
+ self.assertEqual(
+ parents[start_id],
+ search.NULL,
+ msg="Parent of start is -1 (sentinel value for null)",
+ )
+ for start_child in (5, 0, 9):
+ self.assertEqual(
+ parents[start_child],
+ start_id,
+ msg="Child of start should have start as parent",
+ )
+ for parent, child in zip(correct_path[:-1], correct_path[1:]):
+ self.assertEqual(
+ parents[child],
+ parent,
+ msg="Previous node in shortest path should be parent",
+ )
+
+
+class TestAStarSE2(unittest.TestCase):
+ def setUp(self):
+ fname = os.path.join(os.path.dirname(__file__), "share", "map1.txt")
+ permissible_region = np.loadtxt(fname, dtype=bool)
+ self.problem = SE2Problem(permissible_region, curvature=3.0)
+ self.sampler = HaltonSampler(self.problem.extents)
+
+ def test_astar_se2problem(self):
+ num_vertices = 8
+ connection_radius = 5.0
+ lazy = False
+ rm = Roadmap(self.problem, self.sampler, num_vertices, connection_radius, lazy)
+
+ start_id = rm.add_node(np.array([1, 1, 0]), is_start=True)
+ goal_id = rm.add_node(np.array([7, 1, 0]), is_start=False)
+ correct_path = [start_id, 3, goal_id]
+
+ path, parents = search.astar(rm, start_id, goal_id)
+ self.assertEqual(
+ path[0],
+ start_id,
+ msg="Path must begin at start",
+ )
+ self.assertEqual(
+ path[-1],
+ goal_id,
+ msg="Path must end at goal",
+ )
+ self.assertEqual(
+ path,
+ correct_path,
+ msg="A* implementation is incorrect",
+ )
+
+ self.assertEqual(
+ parents[start_id],
+ search.NULL,
+ msg="Parent of start is -1 (sentinel value for null)",
+ )
+ for parent, child in zip(correct_path[:-1], correct_path[1:]):
+ self.assertEqual(
+ parents[child],
+ parent,
+ msg="Previous node in shortest path should be parent",
+ )
+
+
+class TestLazyAStar(unittest.TestCase):
+ def setUp(self):
+ fname = os.path.join(os.path.dirname(__file__), "share", "map1.txt")
+ permissible_region = np.loadtxt(fname, dtype=bool)
+ self.problem = R2Problem(permissible_region, check_resolution=0.5)
+ self.sampler = HaltonSampler(self.problem.extents)
+
+ def test_lazy_astar(self):
+ num_vertices = 10
+ connection_radius = 5.0
+ lazy = True
+ rm = Roadmap(self.problem, self.sampler, num_vertices, connection_radius, lazy)
+
+ start_id = rm.add_node(np.array([1, 1]), is_start=True)
+ goal_id = rm.add_node(np.array([8, 8]), is_start=False)
+ correct_path = [start_id, 9, 2, 7, goal_id]
+
+ path, parents = search.astar(rm, start_id, goal_id)
+ self.assertEqual(
+ path[0],
+ start_id,
+ msg="Path must begin at start",
+ )
+ self.assertEqual(
+ path[-1],
+ goal_id,
+ msg="Path must end at goal",
+ )
+ np.testing.assert_almost_equal(
+ rm.compute_path_length(path),
+ rm.compute_path_length(correct_path),
+ err_msg="Lazy A* implementation is incorrect",
+ )
+ self.assertEqual(
+ path,
+ correct_path,
+ msg="Lazy A* implementation is incorrect",
+ )
+
+ self.assertEqual(
+ parents[start_id],
+ search.NULL,
+ msg="Parent of start is -1 (sentinel value for null)",
+ )
+ for start_child in (5, 0, 9):
+ self.assertEqual(
+ parents[start_child],
+ start_id,
+ msg="Child of start should have start as parent",
+ )
+ for parent, child in zip(correct_path[:-1], correct_path[1:]):
+ self.assertEqual(
+ parents[child],
+ parent,
+ msg="Previous node in shortest path should be parent",
+ )
+
+ self.assertEqual(
+ rm.edges_evaluated, 10, msg="Incorrect number of edges evaluated"
+ )
+
+
+if __name__ == "__main__":
+ rosunit.unitrun("planning", "test_astar_r2", TestAStarR2)
+ rosunit.unitrun("planning", "test_astar_se2", TestAStarSE2)
+ rosunit.unitrun("planning", "test_lazy_astar", TestLazyAStar)
diff --git a/planning/test/problems.py b/planning/test/problems.py
new file mode 100644
index 0000000..79a0dc8
--- /dev/null
+++ b/planning/test/problems.py
@@ -0,0 +1,110 @@
+#!/usr/bin/env python
+
+import numpy as np
+import rosunit
+import unittest
+
+from planning.problems import PlanarProblem, R2Problem
+from planning.samplers import LatticeSampler
+
+
+class TestPlanarProblem(unittest.TestCase):
+ def setUp(self):
+ permissible_region = np.ones((5, 10), dtype=bool)
+ permissible_region[3:7, 3:7] = 0
+ self.problem = PlanarProblem(permissible_region)
+
+ def test_planar_state_validity_extents(self):
+ states = np.array(
+ [
+ [1, 1],
+ [1, -1],
+ [-1, 1],
+ [-1, -1],
+ ]
+ )
+ np.testing.assert_equal(
+ self.problem.check_state_validity(states),
+ np.array([1, 0, 0, 0], dtype=bool),
+ err_msg="States below the extents are not valid",
+ )
+
+ states = np.array(
+ [
+ [9, 4],
+ [9.9, 4.9],
+ [9, 1],
+ [10, 1],
+ [11, 1],
+ [11, 11],
+ [1, 9],
+ [1, 10],
+ [1, 11],
+ ]
+ )
+ np.testing.assert_equal(
+ self.problem.check_state_validity(states),
+ np.array([1, 1, 1, 0, 0, 0, 0, 0, 0], dtype=bool),
+ err_msg="States above the extents are not valid",
+ )
+
+ def test_planar_state_validity_collision(self):
+ permissible_region = np.ones((10, 10), dtype=bool)
+ permissible_region[4:6, 3:7] = 0
+ self.problem = PlanarProblem(permissible_region)
+
+ states = np.array(
+ [
+ [3, 4],
+ [4, 3],
+ [3.1, 4.1],
+ [4.1, 3.1],
+ ]
+ )
+ np.testing.assert_equal(
+ self.problem.check_state_validity(states),
+ np.array([0, 1, 0, 1], dtype=bool),
+ err_msg="States in collision are not valid",
+ )
+
+
+class TestR2Problem(unittest.TestCase):
+ def setUp(self):
+ permissible_region = np.ones((10, 10), dtype=bool)
+ permissible_region[3:7, 3:7] = 0
+ self.problem = R2Problem(permissible_region)
+ self.sampler = LatticeSampler(self.problem.extents)
+
+ def test_r2_heuristic(self):
+ # This sampler creates vertices for x and y = (1.67, 5.0, 8.33).
+ # Only the state (5.0, 5.0) should be in collision.
+
+ num_samples = 9
+ samples = self.sampler.sample(num_samples)
+ valid = self.problem.check_state_validity(samples)
+
+ self.assertEqual(
+ valid.sum(),
+ 8,
+ msg="Only one sample should be in collision",
+ )
+ np.testing.assert_equal(
+ samples[~valid, :],
+ np.array([[5, 5]]),
+ err_msg="The sample in collision should be (5, 5)",
+ )
+
+ samples = samples[valid, :]
+ heuristics = self.problem.compute_heuristic(samples[0, :], samples[1:, :])
+
+ distances = np.linalg.norm(samples[1:, :] - samples[0, :], axis=1)
+ np.testing.assert_allclose(
+ heuristics,
+ distances,
+ err_msg="The R2 heuristic should be the Euclidean norm",
+ )
+
+
+if __name__ == "__main__":
+ rosunit.unitrun("planning", "test_planar_problem", TestPlanarProblem)
+ rosunit.unitrun("planning", "test_r2_problem", TestR2Problem)
diff --git a/planning/test/roadmap.py b/planning/test/roadmap.py
new file mode 100644
index 0000000..6b1facc
--- /dev/null
+++ b/planning/test/roadmap.py
@@ -0,0 +1,185 @@
+#!/usr/bin/env python
+
+import collections
+import numpy as np
+import rosunit
+import unittest
+
+from planning.problems import R2Problem
+from planning.samplers import LatticeSampler, HaltonSampler
+from planning.roadmap import Roadmap
+
+
+class TestRoadmap(unittest.TestCase):
+ def setUp(self):
+ permissible_region = np.ones((10, 10), dtype=bool)
+ permissible_region[3:7, 3:7] = 0
+ self.problem = R2Problem(permissible_region)
+ self.sampler = LatticeSampler(self.problem.extents)
+
+ def _count_outdegree(self, edges, directed):
+ counter = collections.Counter()
+ for (u, v) in edges:
+ counter[u] += 1
+ if not directed:
+ counter[v] += 1
+ return counter
+
+ def test_r2problem_roadmap_edge_collisions(self):
+ # This lattice roadmap has vertices for x and y = (1.67, 5.0, 8.33).
+ # Setting the connection radius to 15 creates a fully-connected graph.
+ num_vertices = 9
+ connection_radius = 15
+ lazy = False
+ rm = Roadmap(
+ self.problem,
+ self.sampler,
+ num_vertices,
+ connection_radius,
+ lazy,
+ )
+ outdegree = self._count_outdegree(rm.graph.edges(), directed=False)
+ for node in range(rm.graph.number_of_nodes()):
+ deg = outdegree.get(node, 0)
+ self.assertGreater(deg, 0, msg="All nodes should be connected")
+
+ # Uncomment this line to visualize the roadmap
+ # rm.visualize(show_edges=True)
+
+ test_node = 1
+ test_state = rm.vertices[test_node, :]
+ self.assertEqual(
+ outdegree[test_node],
+ 2,
+ msg="State {} should be connected to two neighbors".format(test_state),
+ )
+
+ def test_r2problem_roadmap_four(self):
+ # This lattice roadmap has vertices for x and y = (0.5, 1.5, ..., 9.5).
+ # Setting the radius to 1.0 creates a four-connected lattice graph,
+ # excluding vertices that are in collision.
+
+ num_vertices = 100
+ connection_radius = 1.0
+ lazy = False
+ rm = Roadmap(
+ self.problem,
+ self.sampler,
+ num_vertices,
+ connection_radius,
+ lazy,
+ )
+ outdegree = self._count_outdegree(rm.graph.edges(), directed=False)
+ for node in range(rm.graph.number_of_nodes()):
+ deg = outdegree.get(node, 0)
+ self.assertGreater(deg, 0, msg="All nodes should be connected")
+ self.assertLessEqual(
+ deg, 4, msg="All nodes should be connected to at most four neighbors"
+ )
+
+ # These start and goal states are in the center of a lattice cell, so
+ # they should be connected to four neighbors (the corners of the cell).
+ start_state = np.ones(2)
+ start_node = rm.add_node(start_state, is_start=True)
+ goal_state = 9 * np.ones(2)
+ goal_node = rm.add_node(goal_state, is_start=False)
+ outdegree = self._count_outdegree(rm.graph.edges(), directed=False)
+ self.assertEqual(
+ outdegree[start_node], 4, msg="Start should be connected to four neighbors"
+ )
+ self.assertEqual(
+ outdegree[goal_node], 4, msg="Goal should be connected to four neighbors"
+ )
+
+ # Uncomment this line to visualize the roadmap
+ # rm.visualize(show_edges=True)
+
+ def test_r2problem_roadmap_eight(self):
+ # This lattice roadmap has vertices for x and y = (0.5, 1.5, ..., 9.5).
+ # Setting the radius to sqrt(2) creates an eight-connected lattice graph,
+ # excluding vertices that are in collision.
+
+ num_vertices = 100
+ connection_radius = np.sqrt(2)
+ lazy = False
+ rm = Roadmap(
+ self.problem,
+ self.sampler,
+ num_vertices,
+ connection_radius,
+ lazy,
+ )
+ outdegree = self._count_outdegree(rm.graph.edges(), directed=False)
+ for node in range(rm.graph.number_of_nodes()):
+ deg = outdegree.get(node, 0)
+ self.assertGreater(deg, 0, msg="All nodes should be connected")
+ self.assertLessEqual(
+ deg, 8, msg="All nodes should be connected to at most four neighbors"
+ )
+
+ # This start state is on an edge of a lattice cell, so it should be
+ # connected to six neighbors (0.5, 0.5), (1.5, 0.5), (2.5, 0.5),
+ # (0.5, 1.5), (1.5, 1.5), (2.5, 1.5).
+ start_state = np.array([1.5, 1.0])
+ start_node = rm.add_node(start_state, is_start=True)
+ outdegree = self._count_outdegree(rm.graph.edges(), directed=False)
+ self.assertEqual(
+ outdegree[start_node], 6, msg="Start should be connected to four neighbors"
+ )
+
+ # Uncomment this line to visualize the roadmap
+ # rm.visualize(show_edges=True)
+
+ def test_r2problem_roadmap_halton(self):
+ self.sampler = HaltonSampler(self.problem.extents)
+
+ num_vertices = 36
+ connection_radius = 2.5
+ lazy = False
+ rm = Roadmap(
+ self.problem,
+ self.sampler,
+ num_vertices,
+ connection_radius,
+ lazy,
+ )
+
+ outdegree = self._count_outdegree(rm.graph.edges(), directed=False)
+ for node in range(rm.graph.number_of_nodes()):
+ deg = outdegree.get(node, 0)
+ self.assertGreater(deg, 0, msg="All nodes should be connected")
+
+ try:
+ state = 5 * np.ones(2)
+ node = rm.add_node(state, is_start=True)
+ self.fail("state in collision should error")
+ except ValueError:
+ pass
+
+ state = 0.1 * np.ones(2)
+ node = rm.add_node(state, is_start=True)
+ outdegree = self._count_outdegree(rm.graph.edges(), directed=False)
+ self.assertEqual(
+ outdegree[node], 2, msg="Node should be connected to two neighbors"
+ )
+
+ state = np.ones(2)
+ node = rm.add_node(state, is_start=True)
+ outdegree = self._count_outdegree(rm.graph.edges(), directed=False)
+ self.assertEqual(
+ outdegree[node], 5, msg="Node should be connected to five neighbors"
+ )
+
+ state = 9 * np.ones(2)
+ node = rm.add_node(state, is_start=True)
+ outdegree = self._count_outdegree(rm.graph.edges(), directed=False)
+ self.assertEqual(
+ outdegree[node], 4, msg="Node should be connected to four neighbors"
+ )
+
+ # Uncomment this line to visualize the roadmap
+ # rm.visualize(show_edges=True)
+
+
+if __name__ == "__main__":
+ rosunit.unitrun("planning", "test_roadmap", TestRoadmap)
diff --git a/planning/test/samplers.py b/planning/test/samplers.py
new file mode 100644
index 0000000..d71db2f
--- /dev/null
+++ b/planning/test/samplers.py
@@ -0,0 +1,278 @@
+#!/usr/bin/env python
+
+from __future__ import division
+
+import numpy as np
+import rosunit
+import unittest
+
+from planning.samplers import HaltonSampler, LatticeSampler
+
+
+class TestHaltonSampler(unittest.TestCase):
+ def setUp(self):
+ extents = np.zeros((2, 2))
+ extents[:, 1] = 1
+ self.sampler = HaltonSampler(extents)
+ self.batch_size = 1000
+
+ def test_compute_sample_base_two(self):
+ # The Halton sequence for base two begins with
+ # 0.5, 0.25, 0.75, 0.125, 0.625, 0.375, 0.875
+
+ np.testing.assert_allclose(
+ [self.sampler.compute_sample(i, 2) for i in range(0, 1)],
+ np.array([1]) / 2,
+ err_msg="The Halton sequence does not match.",
+ )
+ np.testing.assert_allclose(
+ [self.sampler.compute_sample(i, 2) for i in range(1, 3)],
+ np.array([1, 3]) / 4,
+ err_msg="The Halton sequence does not match.",
+ )
+ np.testing.assert_allclose(
+ [self.sampler.compute_sample(i, 2) for i in range(3, 7)],
+ np.array([1, 5, 3, 7]) / 8,
+ err_msg="The Halton sequence does not match.",
+ )
+ np.testing.assert_allclose(
+ [self.sampler.compute_sample(i, 2) for i in range(7, 15)],
+ np.array([1, 9, 5, 13, 3, 11, 7, 15]) / 16,
+ err_msg="The Halton sequence does not match.",
+ )
+
+ def test_compute_sample_base_three(self):
+ # The Halton sequence for base three begins with
+ # 1/3, 2/3, 1/9, 4/9, 7/9, 2/9, 5/9, 8/9
+
+ np.testing.assert_allclose(
+ [self.sampler.compute_sample(i, 3) for i in range(0, 2)],
+ np.array([1, 2]) / 3,
+ err_msg="The Halton sequence does not match.",
+ )
+ np.testing.assert_allclose(
+ [self.sampler.compute_sample(i, 3) for i in range(2, 8)],
+ np.array([1, 4, 7, 2, 5, 8]) / 9,
+ err_msg="The Halton sequence does not match.",
+ )
+
+ def test_sample_2d(self):
+ batch1 = self.sampler.sample(self.batch_size)
+ self.assertEqual(
+ batch1.shape,
+ (self.batch_size, 2),
+ msg="sample should return the number of samples requested",
+ )
+ np.testing.assert_allclose(
+ (batch1 > 0.5).all(axis=1).sum() / self.batch_size,
+ 0.25,
+ atol=0.001,
+ err_msg="samples are not distributed uniformly across the extents",
+ )
+
+ batch2 = self.sampler.sample(self.batch_size)
+ self.assertEqual(
+ batch2.shape,
+ (self.batch_size, 2),
+ msg="sample should return the number of samples requested",
+ )
+ np.testing.assert_allclose(
+ (batch2 > 0.5).all(axis=1).sum() / self.batch_size,
+ 0.25,
+ atol=0.001,
+ err_msg="samples are not distributed uniformly across the extents",
+ )
+
+ self.assertFalse(
+ np.array_equal(batch1, batch2),
+ msg="Each call to sample should return different samples",
+ )
+
+ def test_sample_2d_scaled_upper_extent(self):
+ extents = np.zeros((2, 2))
+ extents[:, 1] = 10
+ self.sampler = HaltonSampler(extents)
+
+ batch1 = self.sampler.sample(self.batch_size)
+ self.assertEqual(
+ batch1.shape,
+ (self.batch_size, 2),
+ msg="sample should return the number of samples requested",
+ )
+ np.testing.assert_allclose(
+ (batch1 > 5).all(axis=1).sum() / self.batch_size,
+ 0.25,
+ atol=0.001,
+ err_msg="samples are not distributed uniformly across the extents",
+ )
+
+ batch2 = self.sampler.sample(self.batch_size)
+ self.assertEqual(
+ batch2.shape,
+ (self.batch_size, 2),
+ msg="sample should return the number of samples requested",
+ )
+ np.testing.assert_allclose(
+ (batch2 > 5).all(axis=1).sum() / self.batch_size,
+ 0.25,
+ atol=0.001,
+ err_msg="samples are not distributed uniformly across the extents",
+ )
+
+ self.assertFalse(
+ np.array_equal(batch1, batch2),
+ msg="Each call to sample should return different samples",
+ )
+
+ def test_sample_2d_scaled_lower_extent(self):
+ extents = 10 * np.ones((2, 2))
+ extents[:, 0] = -10
+ self.sampler = HaltonSampler(extents)
+
+ batch1 = self.sampler.sample(self.batch_size)
+ self.assertEqual(
+ batch1.shape,
+ (self.batch_size, 2),
+ msg="sample should return the number of samples requested",
+ )
+ np.testing.assert_allclose(
+ (batch1 > 0).all(axis=1).sum() / self.batch_size,
+ 0.25,
+ atol=0.001,
+ err_msg="samples are not distributed uniformly across the extents",
+ )
+
+ batch2 = self.sampler.sample(self.batch_size)
+ self.assertEqual(
+ batch2.shape,
+ (self.batch_size, 2),
+ msg="sample should return the number of samples requested",
+ )
+ np.testing.assert_allclose(
+ (batch2 > 0).all(axis=1).sum() / self.batch_size,
+ 0.25,
+ atol=0.001,
+ err_msg="samples are not distributed uniformly across the extents",
+ )
+
+ self.assertFalse(
+ np.array_equal(batch1, batch2),
+ msg="Each call to sample should return different samples",
+ )
+
+ def test_sample_3d(self):
+ extents = np.zeros((3, 2))
+ extents[:, 1] = 1
+ self.sampler = HaltonSampler(extents)
+
+ batch1 = self.sampler.sample(self.batch_size)
+ self.assertEqual(
+ batch1.shape,
+ (self.batch_size, 3),
+ msg="sample should return the number of samples requested",
+ )
+ np.testing.assert_allclose(
+ (batch1 > 0.5)[:, :2].all(axis=1).sum() / self.batch_size,
+ 0.25,
+ atol=0.005,
+ err_msg="samples are not distributed uniformly across the extents",
+ )
+ np.testing.assert_allclose(
+ (batch1 > 0.5).all(axis=1).sum() / self.batch_size,
+ 0.125,
+ atol=0.005,
+ err_msg="samples are not distributed uniformly across the extents",
+ )
+
+ batch2 = self.sampler.sample(self.batch_size)
+ self.assertEqual(
+ batch2.shape,
+ (self.batch_size, 3),
+ msg="sample should return the number of samples requested",
+ )
+ np.testing.assert_allclose(
+ (batch2 > 0.5)[:, :2].all(axis=1).sum() / self.batch_size,
+ 0.25,
+ atol=0.005,
+ err_msg="samples are not distributed uniformly across the extents",
+ )
+ np.testing.assert_allclose(
+ (batch2 > 0.5).all(axis=1).sum() / self.batch_size,
+ 0.125,
+ atol=0.005,
+ err_msg="samples are not distributed uniformly across the extents",
+ )
+
+ self.assertFalse(
+ np.array_equal(batch1, batch2),
+ msg="Each call to sample should return different samples",
+ )
+
+ def test_sample_3d_scaled(self):
+ extents = np.zeros((3, 2))
+ extents[:, 1] = 10
+ self.sampler = HaltonSampler(extents)
+
+ batch1 = self.sampler.sample(self.batch_size)
+ self.assertEqual(
+ batch1.shape,
+ (self.batch_size, 3),
+ msg="sample should return the number of samples requested",
+ )
+ np.testing.assert_allclose(
+ (batch1 > 5)[:, :2].all(axis=1).sum() / self.batch_size,
+ 0.25,
+ atol=0.005,
+ err_msg="samples are not distributed uniformly across the extents",
+ )
+ np.testing.assert_allclose(
+ (batch1 > 5).all(axis=1).sum() / self.batch_size,
+ 0.125,
+ atol=0.005,
+ err_msg="samples are not distributed uniformly across the extents",
+ )
+
+ batch2 = self.sampler.sample(self.batch_size)
+ self.assertEqual(
+ batch2.shape,
+ (self.batch_size, 3),
+ msg="sample should return the number of samples requested",
+ )
+ np.testing.assert_allclose(
+ (batch2 > 5)[:, :2].all(axis=1).sum() / self.batch_size,
+ 0.25,
+ atol=0.005,
+ err_msg="samples are not distributed uniformly across the extents",
+ )
+ np.testing.assert_allclose(
+ (batch2 > 5).all(axis=1).sum() / self.batch_size,
+ 0.125,
+ atol=0.005,
+ err_msg="samples are not distributed uniformly across the extents",
+ )
+
+ self.assertFalse(
+ np.array_equal(batch1, batch2),
+ msg="Each call to sample should return different samples",
+ )
+
+
+class TestLatticeSampler(unittest.TestCase):
+ def setUp(self):
+ extents = np.zeros((2, 2))
+ extents[:, 1] = 1
+ self.sampler = LatticeSampler(extents)
+ self.batch_size = 9
+
+ def test_sample_2d(self):
+ samples = self.sampler.sample(self.batch_size)
+ self.assertEqual(
+ samples.shape,
+ (self.batch_size, 2),
+ msg="sample should return the number of samples requested",
+ )
+
+
+if __name__ == "__main__":
+ rosunit.unitrun("planning", "test_halton_sampler", TestHaltonSampler)
+ rosunit.unitrun("planning", "test_lattice_sampler", TestLatticeSampler)
diff --git a/planning/test/share/map1.txt b/planning/test/share/map1.txt
new file mode 100644
index 0000000..a8db302
--- /dev/null
+++ b/planning/test/share/map1.txt
@@ -0,0 +1,9 @@
+1 1 1 1 1 1 1 1 1 1 1
+1 1 1 1 1 1 1 1 1 1 1
+1 1 0 0 1 0 0 1 1 1 1
+1 1 0 0 1 0 0 1 1 1 1
+1 1 0 1 1 1 0 1 1 1 1
+1 1 0 1 1 1 0 1 1 1 1
+1 1 0 0 0 0 0 1 1 1 1
+1 1 1 1 1 1 1 1 1 1 1
+1 1 1 1 1 1 1 1 1 1 1
diff --git a/planning/test/share/map2.txt b/planning/test/share/map2.txt
new file mode 100644
index 0000000..c177a1e
--- /dev/null
+++ b/planning/test/share/map2.txt
@@ -0,0 +1,473 @@
+1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
+1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
+1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
+1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
+1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
+1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
+1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
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+1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
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+1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
+1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
diff --git a/planning/test/shortcut.py b/planning/test/shortcut.py
new file mode 100644
index 0000000..4730288
--- /dev/null
+++ b/planning/test/shortcut.py
@@ -0,0 +1,64 @@
+#!/usr/bin/env python
+
+import numpy as np
+import os
+import rosunit
+import unittest
+
+from planning import search
+from planning.samplers import HaltonSampler
+from planning.problems import R2Problem
+from planning.roadmap import Roadmap
+
+
+class TestShortcut(unittest.TestCase):
+ def setUp(self):
+ fname = os.path.join(os.path.dirname(__file__), "share", "map1.txt")
+ permissible_region = np.loadtxt(fname, dtype=bool)
+ self.problem = R2Problem(permissible_region, check_resolution=0.5)
+ self.sampler = HaltonSampler(self.problem.extents)
+
+ def test_shortcut(self):
+ num_vertices = 10
+ connection_radius = 5.0
+ lazy = False
+ rm = Roadmap(self.problem, self.sampler, num_vertices, connection_radius, lazy)
+
+ start_id = rm.add_node(np.array([1, 1]), is_start=True)
+ goal_id = rm.add_node(np.array([8, 8]), is_start=False)
+ correct_path = [start_id, 9, 2, 7, goal_id]
+
+ path, _ = search.astar(rm, start_id, goal_id)
+ np.testing.assert_almost_equal(
+ rm.compute_path_length(path),
+ rm.compute_path_length(correct_path),
+ err_msg="A* implementation is incorrect",
+ )
+ self.assertEqual(
+ path,
+ correct_path,
+ msg="A* implementation is incorrect",
+ )
+
+ shortcut_path = search.shortcut(rm, path)
+ self.assertLess(
+ rm.compute_path_length(shortcut_path),
+ rm.compute_path_length(path),
+ msg="Shortcut should return a shorter path",
+ )
+
+ correct_shortcut_path = [start_id, 2, goal_id]
+ np.testing.assert_almost_equal(
+ rm.compute_path_length(shortcut_path),
+ rm.compute_path_length(correct_shortcut_path),
+ err_msg="Shortcut implementation is incorrect",
+ )
+ self.assertEqual(
+ shortcut_path,
+ correct_shortcut_path,
+ msg="Shortcut implementation is incorrect",
+ )
+
+
+if __name__ == "__main__":
+ rosunit.unitrun("planning", "test_shortcut", TestShortcut)
diff --git a/planning/writeup/README.md b/planning/writeup/README.md
new file mode 100644
index 0000000..185370f
--- /dev/null
+++ b/planning/writeup/README.md
@@ -0,0 +1,4 @@
+# Project 4: Planning [](../../../pipelines/submit-proj4/latest)
+
+Replace this with your own writeup! Please place all figures in this directory.
+
--
GitLab