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.
@article{arxiv.2008.05112,
title = {Dynamically Constrained Motion Planning Networks for Non-Holonomic Robots},
author = {Jacob J. Johnson and Linjun Li and Fei Liu and Ahmed H. Qureshi and Michael C. Yip},
journal= {arXiv preprint arXiv:2008.05112},
year = {2020}
}