Net-Energy Path Planning for Unmanned Surface Vehicles in Time-Varying Wind-Solar Energy Fields
Year: 2026
Type: article
Abstract: 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.
DOI: 10.3390/jmse14212011
Authors: Cheng Chen, Xiyu Gao, Shuang Chen, Ziyu Cao, Guoli Feng
Institution: Naval University of Engineering
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $3,137
OpenAlex ID: W7223865279