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Voronoi Multi_Robot Collaborate Exploration

Introduction

In the unknown environment, the cooperative exploration of multiple robots is completed by Voronoi partition and deep reinforcement learning. The decision-making level assigns different target positions to each mobile robot through Voronoi partition and point selection formula to minimize repeated exploration; The path planning layer uses the method based on deep reinforcement learning to make each mobile robot reach the corresponding target position without collision.

Enviroment

parameter description
system Ubuntu18.04 ; ROS(Melodic); Phython 2.7
simulator Gazebo
display Rviz
simulation car Turtlebot3(Waffle)
senor LiDAR
laser range 0.1 - 3.5 m
angle range -90 - 90
laser numer 24

Run

dependency package

sudo apt-get install ros-melodic-joy ros-melodic-teleop-twist-joy ros-melodic-teleop-twist-keyboard ros-melodic-laser-proc ros-melodic-rgbd-launch ros-melodic-depthimage-to-laserscan ros-melodic-rosserial-arduino ros-melodic-rosserial-python ros-melodic-rosserial-server ros-melodic-rosserial-client ros-melodic-rosserial-msgs ros-melodic-amcl ros-melodic-map-server ros-melodic-move-base ros-melodic-urdf ros-melodic-xacro ros-melodic-compressed-image-transport ros-melodic-rqt-image-view ros-melodic-gmapping ros-melodic-navigation ros-melodic-interactive-markers ros-melodic-multirobot-map-merge

Add

pyyaml、rospkg、pytorch、torchvision、tensorflow 、tensorboard、mpi4py、joblib、gym、pathlib、wandb、Image、setproctitle、imageio

compile & run

> catkin_make 
> source devel/setup.bash
> roslaunch multi_turtlebot3_expore three_turtlebot3_gmapping.launch
> python ddpg_test.py

test

Move & mapping

simulation_gmapping Comparison of construction effects origin&true

Run in other environments

test_env_2_1 Avoid obstacle test_env_2_3

Reference

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Multi-robot collaborative exploration and mapping through Voronoi partition and DRL in unknown environment

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  • Python 64.5%
  • C++ 26.3%
  • CMake 8.3%
  • Other 0.9%