基于即时修复式稀疏A~*算法的动态航迹规划  

Dynamic path planning using anytime repairing sparse A~* algorithm

作　　者：王生印 龙腾[1,2] 王祝[1,2] 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)

机构地区：[1]北京理工大学宇航学院,北京100081 [2]飞行器动力学与控制教育部重点实验室,北京100081

出　　处：《系统工程与电子技术》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

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.

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 

分 类 号：V249[航空宇航科学与技术—飞行器设计]