Multi-Agent Motion Planning using Deep Learning for Space Applications
Abstract
State-of-the-art motion planners cannot scale to a large number of systems. Motion planning for multiple agents is an NP (non-deterministic polynomial-time) hard problem, so the computation time increases exponentially with each addition of agents. This computational demand is a major stumbling block to the motion planner's application to future NASA missions involving the swarm of space vehicles. We applied a deep neural network to transform computationally demanding mathematical motion planning problems into deep learning-based numerical problems. We showed optimal motion trajectories can be accurately replicated using deep learning-based numerical models in several 2D and 3D systems with multiple agents. The deep learning-based numerical model demonstrates superior computational efficiency with plans generated 1000 times faster than the mathematical model counterpart.
Cite
@article{arxiv.2010.07935,
title = {Multi-Agent Motion Planning using Deep Learning for Space Applications},
author = {Kyongsik Yun and Changrak Choi and Ryan Alimo and Anthony Davis and Linda Forster and Amir Rahmani and Muhammad Adil and Ramtin Madani},
journal= {arXiv preprint arXiv:2010.07935},
year = {2020}
}
Comments
2020 AIAA ASCEND