固定翼无人机在线航迹规划方法

在不确定环境下，针对固定翼无人机（UAV）航迹规划问题，提出了一种基于滚动时域控制的模糊粒子群优化算法与改进人工势场法相结合的在线航迹规划方法。首先，对凸多边形障碍物进行最小外接圆拟合；然后，根据静态威胁，将规划问题转化为一系列时域窗口内的在线子问题，利用模糊粒子群算法实时优化求解以实现静态避障；当环境中存在动态威胁时，使用改进人工势场法对航迹进行调整完成动态避障。为了满足固定翼无人机的动态约束，同时提出固定翼UAV的碰撞检测法，可提前判断障碍物是否为真正威胁源，以此减少转弯频率和幅度，降低飞行代价。仿真实验结果表明，所提方法在固定翼UAV航迹规划中能有效提升规划速度、稳定性与实时避障能力，且克服了传统人工势场容易陷入局部最优的缺点。

On-line path planning method of fixed-wing unmanned aerial vehicle

By the combination of fuzzy particle swarm optimization algorithm based on receding horizon control and improved artificial potential field, an on-line path planning method for achieving fixed-wing Unmanned Aerial Vehicle (UAV) path planning in uncertain environment was proposed. Firstly, the minimum circumscribed circle fitting was performed on the convex polygonal obstacles. Then, aiming at the static obstacles, the path planning problem was transformed into a series of on-line sub-problems in the time domain window, and the fuzzy particle swarm algorithm was applied to optimize and solve the sub-problems in real time, realizing the static obstacle avoidance. When there were dynamic obstacles in the environment, the improved artificial potential field was used to accomplish the dynamic obstacle avoidance by adjusting the path. In order to meet the dynamic constraints of fixed-wing UAV, a collision detection method for fixed-wing UAV was proposed to judge whether the obstacles were real threat sources or not in advance and reduce the flight cost by decreasing the turning frequency and range. The simulation results show that, the proposed method can effectively improve the planning speed, stability and real-time obstacle avoidance ability of fixed-wing UAV path planning, and it overcomes the shortcoming of easy to falling into local optimum in traditional artificial potential field method.