Continuous World Coverage Path Planning for Fixed-Wing UAVs using Deep Reinforcement Learning
Journal:
arXiv
Published Date:
May 13, 2025
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.
