Global Energy-Saving Path Planning Method Based on Solar-Energy Capture

Shortest-distance-path planning cannot satisfy the energy requirements of natural-energy-driven unmanned surface vehicles (NSVs) because it ignores the energy that can be harvested from the environment along the route. This paper develops a global energy-saving path planning method for NSVs by embedding a solar-energy capture weight function into the evaluation functions of the Dijkstra and Theta* algorithms. The grid environment is represented with static obstacles and time-varying cloud occlusion and the improved algorithms bias the search toward nodes with higher solar radiation and lower cloud occlusion so that the planned path avoids obstacles while maximizing the captured energy. The proposed algorithms are compared with the traditional Dijkstra, Theta* and Energy Theta* algorithms, which disregard clouds, under low, medium and high cloud cover; dynamic clouds; and a real marine environment. The improved Theta* algorithm achieves energy balance rates of 91.3%, 91.0% and 84.1% in the low-cloud, dynamic cloud and real marine environment scenarios, respectively, whereas the traditional Theta* algorithm reaches only 50.4% in the real marine environment and the improved Dijkstra algorithm reaches 90.2% and 78.5% in the dynamic cloud and real marine environment scenarios, respectively. These results show that the solar-energy capture weight function raises the captured energy and the energy balance rate, thereby extending the endurance of the NSV.