# Dynamically Constrained Motion Planning Networks for Non-Holonomic Robots

**Authors:** Jacob J. Johnson, Linjun Li, Fei Liu, Ahmed H. Qureshi, Michael C. Yip  
**arXiv:** [2008.05112](https://arxiv.org/abs/2008.05112)  
**Date:** 2020-08-12  
**Summary page:** https://www.summarizepaper.com/fr/arxiv-id/2008.05112v1/

## Abstract

Reliable real-time planning for robots is essential in today's rapidly expanding automated ecosystem. In such environments, traditional methods that plan by relaxing constraints become unreliable or slow-down for kinematically constrained robots. This paper describes the algorithm Dynamic Motion Planning Networks (Dynamic MPNet), an extension to Motion Planning Networks, for non-holonomic robots that address the challenge of real-time motion planning using a neural planning approach. We propose modifications to the training and planning networks that make it possible for real-time planning while improving the data efficiency of training and trained models' generalizability. We evaluate our model in simulation for planning tasks for a non-holonomic robot. We also demonstrate experimental results for an indoor navigation task using a Dubins car.