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reciprocal velocity obstacle ros library

Introduction

This ros package is derived from the ORCA library (lib).

Environment

  • Ubuntu 18.04
  • ros Melodic

Install

git clone https://github.com/hanruihua/rvo_ros.git
cd ~/catkin_ws
catkin_make

set environment parameter

export GAZEBO_PLUGIN_PATH=${GAZEBO_PLUGIN_PATH}:~/catkin_ws/devel/lib

Please write this line in the file .bashrc or .zshrc

Usage

rosrun rvo_ros rvo_node args

args: the coordinates of init point. default 0,1 0,2 ...0 10

for example:

rosrun rvo_ros rvo_node 0 1 0 2 0 3

test with simulation

roslaunch rvo_ros rvo_gazebo_agent.launch

Note: Using service to set the model and goals.

Service

rosrun rvo_ros set_goals_client

  • arguments:

    • model:
      • "default": specify a series of point as goals for the agents. The number of goals should be same as the number of agents: 1 1 2 3 4 2.
      • "random": allocate the goals randomly with limit along x and y, only for number: min_x, max_x, min_y, max_y.
      • "circle": allocate the goals with circle shape: circle_point_x, circle_point_y, radius, flag. flag is to set the reverse mode

  • example:

    rosrun rvo_ros set_goals_client default 1 1 1 4 4 4 4 1
    rosrun rvo_ros set_goals_client random 0 5 1 4
    rosrun rvo_ros set_goals_client circle 4 4 4 0

Topics

  • Subscribed Topic

/rvo/model_states (gazebo_msgs/ModelStates)

attention: To avoid the model confusion, only the model name which is like the 'agent+num' style, for example, agent1, agent2, can be regarded as the agent model.

  • Published Topic

/rvo_vel (gazebo_msgs/WorldStates)

Note: only the speed in x, y direction of each agent calculated from the rvo are set in the WorldStates twist part.

author

Han - Han

License

This project is licensed under the MIT License

Overview ([paper])

The approach for reciprocal n-body collision avoidance, where multiple mobile robots need to avoid collisions with each other while moving in a common workspace.

Assumption:

  1. Each robot is assumed to have a simple shape (circular or convex polygon) moving in a two-dimensional workspace.
  2. The robot is holonomic, i.e. it can move in any direction, such that the control input of each robot is simply given by a two-dimensional velocity vector.
  3. Each robot has perfect sensing, and is able to infer the exact shape, position and velocity of obstacles and other robots in the environment.

Advantage:

  1. Do not need communication among robots.
  2. Can tackle the static obstacles.
  3. Can guarantee local collision-free motion for a large number of robots in a cluttered workspace.

Limitation:

  1. The assumption of perfect sensing is hard to perform in real world because of the uncertainties.
  2. Too many parameters to construct the complex model.