ROS package for visualizing Open Motion Planning Library (OMPL) algorithms in 2D with Rviz.

Overview

OMPL 2D Rviz Visualizer

Visualizing, animating and debugging Open Motion Planning Library (OMPL) algorithms in ROS Rviz. The package allows OMPL planners to find paths in R^2 state space with path minimizing objectives and utilizes the occupancy grid maps for collision checking. We have also added a rviz panel plugin to interface with OMPL.

Installation

Build from source

Clone this package into your src folder of catkin_workspace:

cd 
   
    /src
git clone https://github.com/mlsdpk/ompl_2d_rviz_visualizer.git

   

Install necessary dependencies by running the following command in the root of your workspace:

cd ..
rosdep install -y --from-paths src --ignore-src --rosdistro 
   

   

Then compile with catkin:

catkin_make

Rviz Control Panel for interfacing with OMPL

We provide an easy-to-use rviz panel plugin for interfacing with OMPL.

Maps

Several occupancy grid maps are specifically chosen for testing OMPL planners, which can be found in most sampling-based planning literatures. In order to select a map, user has to manually provide the name of the map to the map_name argument in the launch file. Current version of the visualizer provides four different maps:

  1. empty_map
  2. map_with_single_cube
  3. map_with_many_homotopy_classes
  4. map_with_single_narrow_passage_gap
1 2 3 4

Usage

Run the following launch file to use the visualizer. You can directly pass map names via command line or change it manually in the file.

source 
   
    /devel/setup.bash
roslaunch ompl_2d_rviz_visualizer_ros visualizer.launch map_name:=empty_map

   
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Comments
  • Add animation mode functionality

    Add animation mode functionality

    • Allow users to visualize the animation of exploration progress of planning algorithms
    • Support both time and iteration termination conditions
    • Animation mode only allows iteration termination condition
    opened by mlsdpk 0
Owner
Phone Thiha Kyaw
who loves building autonomous robots and artificially intelligent agents based on reinforcement learning.
Phone Thiha Kyaw
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