An Intelligent Approach to UAV Path Planning with Battery Optimization and Charging Station Selection

Unmanned Aerial Vehicles (UAVs) are widely utilized in surveillance, delivery, and disaster management. However, their operational efficiency is constrained by the limited battery capacity, which poses a significant challenge for prolonged missions. This paper proposes an intelligent path-planning framework that optimizes UAV navigation while ensuring efficient energy utilization and dynamic charging station selection. The proposed approach integrates the A* algorithm for optimal path computation with a battery-aware decision-making mechanism, enabling UAVs to autonomously reroute to charging stations as needed. A comprehensive mathematical formulation of the problem is presented, and simulation results demonstrate the method’s effectiveness in minimizing travel distance while ensuring uninterrupted mission execution. Compared to traditional A* path planning, our battery-aware method reduces mission interruptions by 25% and improves energy efficiency, particularly in dense obstacle scenarios.