Entropy-Based Incremental Coverage Path Planning for Multi-UAV Persistent Monitoring

Abstract

Oil spills continuously affect marine ecosystems and require rapid monitoring for effective emergency response. This letter tackles the problem of persistent monitoring for continuously changing and scattered oil spill regions through Entropy-Based Incremental Coverage Path Planning (EICPP). By using contour comparison between monitoring cycles, an incremental coverage mechanism is first introduced to focus on newly emerged oil spill regions. Then, a balanced region division algorithm is incorporated to handle scattered oil spill areas while ensuring equal workload distribution among UAVs. The entropy-based path planning enhances oil spill monitoring effectiveness by Drift Information Freshness (DIF) through prioritizing high-entropy regions under limited UAV resources. We evaluate the robustness and effectiveness of our method across multiple scenarios. Our method demonstrates clear advantages in DIF, achieving 19–25% improvements over strong baselines across different spill scales and about 19.6–24% on real-world oil spill datasets. It also substantially reduces total flight distance while consistently satisfying the 90% coverage requirement.