摘要
对于城市环境下的无人机路径规划问题,提出了一种多目标路径规划模型,以无人机运行效率最大化与运行风险最小化为目标函数,综合考虑无人机性能约束、噪声影响以及避障要求,使用随机速度更新、惯性因子动态调整等策略对多目标粒子群算法进行改进,基于帕累托解集拥挤距离更新个体极值和群体极值,并构建帕累托解以得到多组备选路径方案。最后通过仿真验证了所提方法的可行性和有效性。
Aiming at the problem of unmanned aerial vehicle(UAV)path planning in urban environments,a multi objective path planning model is proposed.This model aims to maximize UAV operational efficiency while minimizing operational risk,considering UAV performance constraints,noise constraints,and obstacle avoidance requirements.Furthermore,using random speed update and inertia factor dynamic adjustment strategy to improve the particle swarm optimization algorithm.The individual and group extreme values are updated based on the crowded distance of the Pareto solution set,and the Pareto solution set is constructed to obtain multiple sets of candidate path solutions.Finally,the feasibility and effectiveness of the proposed method are verified through simulation.
作者
胡锦标
李加琛
张智杰
羊钊
HU Jin-biao;LI Jia-chen;ZHANG Zhi-jie;YANG Zhao(Nanjing University of Aeronautics and Astronautics,Nanjing 211000,China)
出处
《航空计算技术》
2024年第5期38-42,共5页
Aeronautical Computing Technique
基金
国家自然科学基金资助(52172328)
南京航空航天大学国家级大学生创新创业训练计划项目资助(202310287096Z)。
关键词
城市环境
无人机路径规划
多目标优化
多目标粒子群算法
帕累托解
urban environment
UAV path planning
multi objective optimization
multi objective particle swarm optimization(PSO)
Pareto solution
