Continuous World Coverage Path Planning for Fixed-Wing UAVs using Deep Reinforcement Learning

TL;DR

This paper introduces a continuous environment formulation for UAV coverage path planning, utilizing deep reinforcement learning with a self-adaptive curriculum to optimize power-efficient coverage strategies.

Contribution

It presents a novel continuous motion planning framework for fixed-wing UAVs using reinforcement learning with curvature constraints and adaptive training.

Findings

Effective energy-efficient coverage strategies learned

Outperforms traditional grid-based methods

Demonstrated on diverse scenarios

Abstract

Unmanned Aerial Vehicle (UAV) Coverage Path Planning (CPP) is critical for applications such as precision agriculture and search and rescue. While traditional methods rely on discrete grid-based representations, real-world UAV operations require power-efficient continuous motion planning. We formulate the UAV CPP problem in a continuous environment, minimizing power consumption while ensuring complete coverage. Our approach models the environment with variable-size axis-aligned rectangles and UAV motion with curvature-constrained B\'ezier curves. We train a reinforcement learning agent using an action-mapping-based Soft Actor-Critic (AM-SAC) algorithm employing a self-adaptive curriculum. Experiments on both procedurally generated and hand-crafted scenarios demonstrate the effectiveness of our method in learning energy-efficient coverage strategies.