Optimizing UAV Network Efficiency: Integrative Strategies for Simultaneous Energy Management and Obstacle-Aware Routing

This paper addresses a pivotal challenge in Unmanned Aerial Vehicle (UAV) networks crucial for sectors including delivery services, agriculture, and emergency response: optimizing UAV charging strategies for continuous operation in monitoring and inspection missions. We introduce a novel Maximum Entropy Principle (MEP) framework that employs mobile charging vehicles (MCVs), potentially Unmanned Ground Vehicles (UGVs), for in-field UAV charging. This MEP framework marks a significant advancement in the field by providing an integrated solution for multi-objective problems, including simultaneous obstacle-aware path planning and resource management under energy limitations. Distinguished from traditional heuristic approaches, our framework adeptly handles complex scenarios, significantly reducing optimization variables and facilitating robustness analysis. Through comprehensive simulations, our methodology has demonstrated significant advantages over existing algorithms in handling complex operational scenarios. Unlike other algorithms, which often struggle with large network sizes or intricate constraints, our solution consistently delivers robust performance. Empirical evidence indicates that our method achieves more than double the cost-effectiveness compared to its counterparts, coupled with multiple orders of magnitude faster operational speed. This research not only contributes to the theoretical understanding of autonomous UAV/UGV network planning but also has significant practical implications for real-time, field-deployable solutions.