基于启发式增量搜索的无人机动态航迹规划  

UAV dynamic path planning method via heuristic incremental search

作　　者：李俊志 龙腾[1,2,3,4] 孙景亮 罗野 周桢林 Junzhi LI;Teng LONG;Jingliang SUN;Ye LUO;Zhenlin ZHOU(School of Aerospace Engineering,Beijing Institute of Technology,Beijing 100081,China;Key Laboratory of Dynamics and Control of Flight Vehicle of Ministry of Education,Beijing Institute of Technology,Beijing 100081,China;Beijing Institute of Technology Chongqing Innovation Center,Chongqing 401121,China;National Key Laboratory of Land and Air Based Information Perception and Control,Beijing 100081,China)

机构地区：[1]北京理工大学宇航学院,北京100081 [2]北京理工大学飞行器动力学与控制教育部重点实验室,北京100081 [3]北京理工大学重庆创新中心,重庆401121 [4]陆空基信息感知与控制全国重点实验室,北京100081

出　　处：《中国科学:信息科学》2025年第4期931-948,共18页Scientia Sinica(Informationis)

基　　金：国家自然科学基金(批准号:52372347);北京理工大学青年教师学术启动计划(批准号:XSQD-202201005)资助项目。

摘　　要：面向动态环境下无人机航迹规划的高时效性和动态环境适应性需求,开展基于启发式增量搜索的航迹规划方法研究.将稀疏A^(*)(Sparse A^(*),SA^(*))与增量式搜索结合:通过改进节点扩展规则、定制节点简并策略,实现对SA^(*)历史规划的节点图和代价信息的复用,避免计算资源的浪费;通过定制启发式增量搜索框架,在历史规划信息的基础上进行局部增量式更新,提出了增量式动态稀疏A^(*)算法(incremental dynamic sparse A^(*),ID-SA^(*)),提升航迹规划的动态环境适应性.数值仿真与飞行试验结果表明,ID-SA^(*)能够利用历史规划信息加速动态环境下的航迹重规划,效率远高于SA^(*)、即时修复式稀疏A^(*)(anytime repairing sparse A^(*),AR-SA^(*)),耗时降至10^(-1)s量级,满足有限机载运算资源下的高效动态航迹规划需求.To meet the requirements of high efficiency and dynamic environmental adaptability for UAV path planning in dynamic environments,the dynamic path planning method via incremental heuristic search is investigated.Our approach incorporates the sparse A^(*)(SA^(*))algorithm with incremental search.To reuse historical path planning information of SA^(*),the improved node expansion rules and node degeneracy strategy are introduced,avoiding the wastage of computing resources.Using a modified heuristic incremental search framework,the historical planning information is incrementally updated locally to adapt to dynamic environments,and the incremental dynamic sparse A^(*)algorithm(ID-SA^(*))is proposed.The results of simulation and flight tests show that the proposed method can utilize historical information to speed up UAV path planning in dynamic environments.Compared with the standard SA^(*)and anytime repairing sparse A^(*)(AR-SA^(*)),ID-SA^(*)outperforms the competitors in terms of solving efficiency for dynamic path planning problems,which significantly reduces the computational time to 10^(−1)s,satisfying the requirements of fast dynamic path planning in the limited onboard computational resources.

关 键 词：无人机 航迹规划 动态环境 启发式增量搜索 稀疏A^(*)算法 

分 类 号：V279[航空宇航科学与技术—飞行器设计]