Learning to Recharge: UAV Coverage Path Planning through Deep Reinforcement Learning

TL;DR

This paper introduces a deep reinforcement learning method for UAV coverage path planning that accounts for battery recharging, enabling efficient long-term coverage strategies with improved performance over heuristics.

Contribution

It proposes a novel PPO-based DRL approach with map-based observations, action masking, and position history to handle recharge decisions in UAV coverage planning.

Findings

Outperforms baseline heuristics in coverage efficiency

Generalizes to different target zones and maps

Provides insights into DRL design for long-horizon tasks

Abstract

Coverage path planning (CPP) is a critical problem in robotics, where the goal is to find an efficient path that covers every point in an area of interest. This work addresses the power-constrained CPP problem with recharge for battery-limited unmanned aerial vehicles (UAVs). In this problem, a notable challenge emerges from integrating recharge journeys into the overall coverage strategy, highlighting the intricate task of making strategic, long-term decisions. We propose a novel proximal policy optimization (PPO)-based deep reinforcement learning (DRL) approach with map-based observations, utilizing action masking and discount factor scheduling to optimize coverage trajectories over the entire mission horizon. We further provide the agent with a position history to handle emergent state loops caused by the recharge capability. Our approach outperforms a baseline heuristic, generalizes to different target zones and maps, with limited generalization to unseen maps. We offer valuable insights into DRL algorithm design for long-horizon problems and provide a publicly available software framework for the CPP problem.