In response to practical application challenges in utilizing solar-powered unmanned aerial vehicle(UAV)for remote sensing,this study presents a three-dimensional path planning method tailored for urban-mountainous env...In response to practical application challenges in utilizing solar-powered unmanned aerial vehicle(UAV)for remote sensing,this study presents a three-dimensional path planning method tailored for urban-mountainous environment.Taking into account constraints related to the solar-powered UAV,terrain,and mission objectives,a multi-objective trajectory optimization model is transferred into a single-objective optimization problem with weight factors and multiconstraint and is developed with a focus on three key indicators:minimizing trajectory length,maximizing energy flow e±ciency,and minimizing regional risk levels.Additionally,an enhanced sparrow search algorithm incorporating the Levy flight strategy(SSA-Levy)is introduced to address trajectory planning challenges in such complex environments.Through simulation,the proposed algorithm is compared with particle swarm optimization(PSO)and the regular sparrow search algorithm(SSA)across 17 standard test functions and a simplified simulation of urban-mountainous environments.The results of the simulation demonstrate the superior effectiveness of the designed improved SSA based on the Levy flight strategy for solving the established single-objective trajectory optimization model.展开更多
基金supported in part by the National Natural Science Foundation of China under Grant 51979275the National Key Research and Development Program of China under Grant 2022YFD2001405+8 种基金the open fund of Key Laboratory of Intelligent Equipment and Robotics for Agriculture of Zhejiang Province under Grant 2023ZJZD2306the Key Laboratory of Spatial-temporal Big Data Analysis and Application of Natural Resources in Megacities,Ministry of Natural Resources,under Grant KFKT-2022-05in part by Shenzhen Science and Technology Program(grant number ZDSYS20210623091808026)the Open Project Program of State Key Laboratory of Virtual Reality Technology and Systems,Beihang University,under Grant VRLAB2022C10in part by the open fund project of State Key Laboratory of Clean Energy Utilization under Grant ZJUCEU2022002the open fund of Key Laboratory of Smart Agricultural Technology(Yangtze River Delta),Ministry of Agriculture and Rural Affairs,under Grant KSAT-YRD2023005the Open Project Program of Key Laboratory of Smart Agricultural Technology in Tropical South China,Ministry of Agriculture and Rural Affairs,under Grant HNZHNYKFKT-202202the Higher Education Scientific Research Planning Project,China Association of Higher Education,under Grant 23XXK0304the 2115 Talent Development Program of China Agricultural University.Ben Ma received the master's degree in mechatronics engineering at the College of Engineering,China Agricultural University,Beijing,China,in 2021.
文摘In response to practical application challenges in utilizing solar-powered unmanned aerial vehicle(UAV)for remote sensing,this study presents a three-dimensional path planning method tailored for urban-mountainous environment.Taking into account constraints related to the solar-powered UAV,terrain,and mission objectives,a multi-objective trajectory optimization model is transferred into a single-objective optimization problem with weight factors and multiconstraint and is developed with a focus on three key indicators:minimizing trajectory length,maximizing energy flow e±ciency,and minimizing regional risk levels.Additionally,an enhanced sparrow search algorithm incorporating the Levy flight strategy(SSA-Levy)is introduced to address trajectory planning challenges in such complex environments.Through simulation,the proposed algorithm is compared with particle swarm optimization(PSO)and the regular sparrow search algorithm(SSA)across 17 standard test functions and a simplified simulation of urban-mountainous environments.The results of the simulation demonstrate the superior effectiveness of the designed improved SSA based on the Levy flight strategy for solving the established single-objective trajectory optimization model.
