Net-Energy Path Planning for Unmanned Surface Vehicles in Time-Varying Wind-Solar Energy Fields

To address the spatiotemporal variations of wind and solar resources, the energy consumption of unmanned surface vehicles (USVs), and island–reef obstacle constraints in offshore mobile energy resupply missions, this paper proposes a dynamic multi-objective path planning method oriented toward net energy. A fine-grained obstacle grid is constructed based on satellite imagery, and an hourly ERA5-based wind–solar energy field is constructed independently of the ANP suitability assessment. Energy harvesting, propulsion energy consumption, and onboard base loads are incorporated into a unified energy balance model, with net energy and voyage distance as the optimization objectives. A receding-horizon ME-DMOEA is employed to dynamically optimize the remaining path, and dual-scale grids are used for obstacle detection and local repair. After fine-grid path repair, ME-DMOEA achieved net-energy improvements of 35.83 Wh in S1 and 82.82 Wh in S2 relative to the implemented four-neighbor A* baseline. In 30 paired runs, ME-DMOEA and NSGA-II tied in S1, while ME-DMOEA achieved higher mean net energy in S2 under both tested ERA5 environments. The rolling ON/OFF net-energy difference was not statistically significant. These simulations support net-energy-aware planning under modeled static-obstacle and terminal constraints, without establishing COLREGs-compliant collision avoidance or real-vessel performance.