航空学报2026，Vol.47Issue(12)：206-226,21.DOI:10.7527/S1000-6893.2025.32817
基于多目标强化学习的太阳能无人机航迹规划
Trajectory planning of solar powered unmanned aerial vehicles based on multi-objective reinforcement learning
徐体超 1蒙文跃 2张健1
作者信息
- 1. 中国科学院大学 星际航行学院,北京 100049||中国科学院 工程热物理研究所,北京 100049||轻型动力全国重点实验室,北京 100190
- 2. 中国科学院 工程热物理研究所,北京 100049||轻型动力全国重点实验室,北京 100190
- 折叠
摘要
Abstract
There is a significant coupling between the influencing factors of high-altitude long-endurance solar-powered UAVs in harvesting solar energy and gradient wind energy,and optimizing the harvesting efficiency of these two types of energy simultaneously often leads to conflicts.To address this issue,this study proposes a trajectory planning method based on multi-objective reinforcement learning.This method adopts the multi-objective Soft Actor-Critic(SAC)algorithm based on the multi-objective Markov decision process,combines the UAV's energy harvesting power and energy consumption power into a reward vector,and adds randomly generated weights in each update step.The converged trained policy network can output thrust,attack angle,and bank angle commands based on flight information and a given weight vector,enabling the generation of a set of energy-optimal trajectory solutions within the weight space.Simulation results show that compared with the minimum energy consumption strategy and the strategy based on the conventional single-objective SAC algorithm,this method consistently achieves better energy optimiza-tion efficiency and can adaptively respond to weight changes of energy objectives.Compared with the offline optimized trajectory solution set based on the Non-dominated Sorting Genetic Algorithm Ⅱ,this method achieves a hypervolume of the trajectory solution set reaching 90.07%of the former while maintaining excellent real-time performance.In addi-tion,this method also demonstrates a certain degree of generalization ability and can adapt to new untrained wind fields.关键词
太阳能无人机/航迹规划/动态滑翔/能量优化/强化学习/多目标强化学习Key words
solar-powered unmanned aerial vehicle/trajectory planning/dynamic soaring/energy optimization/re-inforcement learning/multi-objective reinforcement learning分类
航空航天引用本文复制引用
徐体超,蒙文跃,张健..基于多目标强化学习的太阳能无人机航迹规划[J].航空学报,2026,47(12):206-226,21.
