Efficient UAV Trajectory-Planning using Economic Reinforcement Learning

TL;DR

This paper presents REPlanner, a multi-agent reinforcement learning system inspired by economic transactions, for efficient UAV trajectory planning in complex environments with obstacles and POIs, demonstrating improved performance over traditional methods.

Contribution

Introduction of REPlanner, a novel economic reinforcement learning algorithm that coordinates UAVs through auction-based task sharing in a multi-agent setting.

Findings

REPlanner outperforms conventional RL-based trajectory search.

System is highly resilient to changes in swarm size.

Effective coordination emerges from economic game architecture.

Abstract

Advances in unmanned aerial vehicle (UAV) design have opened up applications as varied as surveillance, firefighting, cellular networks, and delivery applications. Additionally, due to decreases in cost, systems employing fleets of UAVs have become popular. The uniqueness of UAVs in systems creates a novel set of trajectory or path planning and coordination problems. Environments include many more points of interest (POIs) than UAVs, with obstacles and no-fly zones. We introduce REPlanner, a novel multi-agent reinforcement learning algorithm inspired by economic transactions to distribute tasks between UAVs. This system revolves around an economic theory, in particular an auction mechanism where UAVs trade assigned POIs. We formulate the path planning problem as a multi-agent economic game, where agents can cooperate and compete for resources. We then translate the problem into a Partially Observable Markov decision process (POMDP), which is solved using a reinforcement learning (RL) model deployed on each agent. As the system computes task distributions via UAV cooperation, it is highly resilient to any change in the swarm size. Our proposed network and economic game architecture can effectively coordinate the swarm as an emergent phenomenon while maintaining the swarm's operation. Evaluation results prove that REPlanner efficiently outperforms conventional RL-based trajectory search.