Multi-Agent Motion Planning using Deep Learning for Space Applications

TL;DR

This paper introduces a deep learning approach to multi-agent motion planning in space applications, significantly reducing computation time and enabling scalable solutions for large swarms of space vehicles.

Contribution

The paper presents a novel deep neural network model that transforms complex multi-agent motion planning problems into efficient numerical solutions, outperforming traditional methods.

Findings

Deep learning models accurately replicate optimal trajectories in 2D and 3D systems.

The proposed method generates plans 1000 times faster than traditional mathematical models.

The approach scales better with the number of agents, addressing computational challenges.

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.