基于多策略SSO和改进A*算法的无人机动态航迹规划  

Dynamic Path Planning for UAVs Based on Multistrategy SSO and Improved A* Algorithm

作　　者：田阔[1] 刘旭[1] TIAN Kuo LIU Xu(Chinese Flight Test Establishment, Xi＇an 710089, China)

机构地区：[1]中国飞行试验研究院,西安710089

出　　处：《电光与控制》2017年第11期31-37,共7页Electronics Optics & Control

摘　　要：针对无人机遇到突发威胁动态航迹规划问题,提出了一种基于多策略SSO和改进A*算法的无人机动态航迹规划方法。该方法将无人机航迹规划划分为静态航迹规划和突发威胁实时规避两个阶段:首先,对于静态航迹规划阶段,采用多策略SSO优化算法对极坐标航迹规划模型进行求解,通过引入完全弹性碰撞、自适应跳跃等机制,在有效满足飞行性能约束的同时,提高了航迹规划结果的可行性;其次,对于突发威胁实时规避阶段,采用改进A*算法对局部区域进行航迹重规划,通过拓展A*算法搜索邻域个数和引入最小"弯折"估计代价函数,在保证实时性要求的同时,能够规划出更加平滑的最优航迹。仿真结果表明,提出的方法能够有效地给出更为满意的无人机动态航迹规划路线。In order to solve the problems of dynamic path planning for Unmanned Aerial Vehicles（UAVs）confronted with sudden threats,a dynamic UAV path-planning method is proposed based on multi-strategy SSO（Social Spider Optimization） and the improved A＊algorithm.The UAV path planning is divided into two stages： static path planning and real-time evasion under sudden threat.At the stage of static path planning,multi-strategy SSO optimization algorithm is used to solve the polar-coordinate path-planning model,and the mechanisms of perfect elastic collision and adaptive skipping are introduced,which can improve the feasibility of the path-planning results while satisfying the restraints of flight performance.At the stage of real-time evasion under unexpected threat,the improved A＊algorithm is used for path planning in local regions for a second time.Through expanding the number of neighborhood for A＊algorithm and introducing the minimum ＂bent＂estimation cost function,a smoother optimal path can be obtained while satisfying the real-time requirements.The simulation results show that the proposed method can provide more satisfactory dynamic path-planning for UAVs.

关 键 词：航迹规划 无人机 群居蜘蛛优化 改进A＊算法 

分 类 号：V19[航空宇航科学与技术—人机与环境工程]