Route planning is a key technology in reconnaissance and combat missions for UAV, and the performance of planning algorithm directly affects its quality.For the optimality and real-time problem in route planning, this paper proposes an improved particle swarm optimization (PSO) algorithm including inertia weight adjustment strategy based on step function and escaping strategy from local minimum.Introducing the concept of SOMR (Surface of Minimum Risk) and defining a specific particle swarm coding representation, the searching space is reduced.For the re-planning online problem, a weight coefficient for re-starting point is defined to balance the conflict between re-planning time and the route optimality.The simulation results demonstrate that 3D route planning for UAV based on this improved PSO can find a more optimal route than typical improvement strategy of PSO and meet the request of re-planning in real time.%航迹规划是无人机执行侦察和作战任务中的关键技术,规划算法的性能直接影响着航迹规划的质量.针对航迹规划最优性和实时性问题,提出一种惯性权值"阶梯式"调整策略与跳出局部最优解策略相结合的改进粒子群无人机航迹规划算法.通过引入最小威胁曲面的概念,使用特定的粒子群位置编码方式将约束条件和搜索算法相结合,缩小了搜索空间.针对无人机在线航迹重规划问题,提出定义重规划起始点权重系数来平衡重规划运算时间和航迹最优性之间矛盾的方法.由基于改进粒子群算法的离线航迹规划与在线航迹重规划仿真对比结果表明,该方法比粒子群算法典型改进方案能够搜索到更优离线航迹,且满足在线航迹重规划的实时性要求.
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