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Dynamic collision avoidance using LSTM to predict time-dependent obstacle behaviors

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vincekurtz/rnn_collvoid

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Predicting Obstacle Motion with a Recurrent Network for Dynamic Collision Avoidance

Multi-agent Collision Avoidance Image

An LSTM network predicts obstacle motion from realtime observations and uses these predictions for collision avoidace. Further details are available in this paper.

Dependencies

  • python
  • ROS Kinetic
  • Stage simulator
  • stage_ros
  • tensorflow
  • GPy

Installation

Install the dependencies

Clone this repo to your catkin workspace source folder

cd [catkin_ws]/src
git clone https://github.com/vincekurtz/rnn_collvoid

Build the project:

cd [catkin_ws]
catkin_make

Usage

Make predictions in real-time, and make a plot of predictions afterwards:

roslaunch rnn_collvoid predict.launch

Make predictions in real-time, and use them to control a robot:

roslaunch rnn_collvoid control.launch

Use this prediction system on two robots to avoid a collision with each other

roslaunch rnn_collvoid multi_agent.launch

With the simulation running, visualize what's happening with rviz:

roslaunch rnn_collvoid rviz.launch

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Dynamic collision avoidance using LSTM to predict time-dependent obstacle behaviors

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