Multi-Agent Motion Planning using Deep Learning for Space Applications
This addresses the computational bottleneck for NASA missions involving swarms of space vehicles, offering a significant speedup but is incremental as it applies existing deep learning to a known problem.
The paper tackled the NP-hard problem of multi-agent motion planning for space applications by using a deep neural network to transform mathematical problems into numerical models, achieving plans 1000 times faster than traditional methods while accurately replicating optimal trajectories in 2D and 3D systems.
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.