测控技术2026，Vol.45Issue(4)：30-38,9.DOI:10.19708/j.ckjs.2025.09.245
基于双向稀疏A*算法的无人机复杂环境路径规划
Path Planning for Complex Environments of UAV Based on Bidirectional Sparse A* Algorithm
摘要
Abstract
Unmanned aerial vehicles(UAVs)have the characteristics of hovering and flexible maneuverability.When operating in complex environments,UAVs will encounter a large number of obstacles and various threat areas.Therefore,it is necessary to plan a feasible path that bypasses the threat area and meets flight con-straints.When searching for a path in a complex environment grid,if the search area is not effectively restrict-ed,it may lead to blind exploration throughout the entire space,and the planned initial path may not meet the actual flight requirements of the UAV,making subsequent flights difficult to execute.Therefore,a path planning method for complex environments of UAVs based on bidirectional sparse A* algorithm is proposed.The bidi-rectional sparse A* algorithm is used to integrate the flight constraints of the UAV(such as step size,turning angle,and flight distance),and the search area of the A* algorithm is limited to a sector to avoid blind search in the entire search space,so an initial path is planned in the grid of complex environments.Combining the bi-directional sparse A* algorithm with ant colony optimization algorithm,introducing bidirectional parallel search mechanism and adaptive pheromone volatilization factor,the global search capability of ant colony optimization algorithm is further optimized.Under the condition of satisfying flight constraints,combining heuristic function and elite retention strategy,the optimal path that can bypass the threat area is planned based on the initial path.Considering that the planned path may have turning points or sharp peaks,a three-dimensional Bezier curve is introduced to smooth the optimal path of the UAV by adjusting the number and position of path control nodes,to reduce UAV turning energy consumption and improve turning efficiency.The experimental results show that the proposed method has fewer traversal nodes and final path nodes,and has a faster convergence speed and shorter path after convergence.关键词
无人机/双向稀疏A*算法/蚁群优化算法/复杂环境/路径规划Key words
UAV/bidirectional sparse A* algorithm/ant colony optimization algorithm/complex environment/path planning分类
航空航天引用本文复制引用
宋昆,王兵..基于双向稀疏A*算法的无人机复杂环境路径规划[J].测控技术,2026,45(4):30-38,9.基金项目
江苏省现代教育技术研究2021年度智慧校园专项课题(2021-R-96766) （2021-R-96766）
