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

Related tags

Graphics ompl_2d_rviz_visualizer
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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