摘要
针对动态环境下无人机航迹规划对时效性、可行性和最优性的需求,将稀疏A~*搜索(sparse A~*search,SAS)算法嵌入到即时修复式架构,并在航迹迭代改善过程中引入双排序准则、存储空间约束及变步长策略,提出了即时修复式稀疏A~*(anytime repairing SAS,AR-SAS)算法。静态环境下蒙特卡罗仿真结果表明AR-SAS算法生成可行航迹与最优航迹的时间都小于标准SAS和分层SAS算法;动态仿真结果表明AR-SAS算法能够快速生成可行航迹,并在规定时间内不断提高航迹最优性,满足动态航迹规划的需求。
To satisfy the requirements of efficiency,feasibility,and optimality of unmanned aerial vehicle path planning in dynamic environment,an anytime repairing sparse A~＊search（AR-SAS）algorithm is proposed,by incorporating the sparse A~＊search（SAS）into anytime repairing framework and introducing double-criteria ordering,memory-bounded and adaptive-step expanding strategies into the process of path optimization.Monte-Carlo simulations in static environment demonstrate that AR-SAS takes less time to generate the feasible path and optimal path compared with standard SAS and hierarchical SAS.Simulation results in dynamic environment show that AR-SAS can satisfy the requirements of dynamic planning to rapidly produce a feasible path and gradually improve the path quality in given time.
作者
王生印
龙腾
王祝
蔡祺生
WANG Shengyin;LONG Teng;WANG Zhu;CAI Qisheng(School of Aerospace Engineering,Beijing Institute of Technology,Beijing 100081,China;Key Laboratory of Dynamics and Control of Flight Vehicle,Ministry of Education,Beijing 100081,China)
出处
《系统工程与电子技术》
EI
CSCD
北大核心
2018年第12期2714-2721,共8页
Systems Engineering and Electronics
基金
国家自然科学基金(51675047)
航空科学基金(2015ZA72004)
中国博士后科学基金(2018M631361)资助课题
关键词
无人机
航迹规划
动态环境
稀疏A^＊算法
即时修复式架构
unmanned aerial vehicle （UAV）
path planning
dynamic environment
sparse A ^＊ search
anytime repairing framework
