Optimizing Unmanned Aerial Vehicle Battery Management for Energy‐Aware Mapping Missions

Unmanned aerial vehicles are widely used for mapping and surveillance applications. A challenging task in mission planning is managing battery use, especially when mapping large areas in a single operation. Mission planning in this case leads to an optimization problem that is generally computationally demanding and intractable in practice. This study proposes a new optimization formulation that addresses these drawbacks. The proposed strategy is aimed at reducing total flight duration by scheduling necessary battery replacements under energy constraints due to limited battery capacities. The approach also optimizes the locations of base stations for battery replacements to minimize the overall mission flight distance, avoiding unnecessary long flights to and from the base. The developed strategy is validated in two real‐world mapping missions. In each case, the optimal solution is compared with a benchmark in which the mapping area is divided according to the planned battery capacities. Both simulation and experimental results demonstrate that the proposed approach reduces the number of required battery replacements and the total flight and mission duration by up to 20%. Moreover, mission safety is enhanced by reducing the number of high‐risk takeoffs and landings. These findings demonstrate that the proposed optimization strategy improves both efficiency and safety in unmanned aerial vehicle mission planning.