基于粒子群算法的固定时间多约束无人机轨迹规划

针对粒子群算法在轨迹规划时，将无人机视为质点，未考虑无人机的飞行时间、角度等参数的不足，提出一种数值方法结合粒子群算法的轨迹规划求解方法。首先，考虑到对每个时刻控制变量进行优化会耗费大量的时间，将无人机的飞行时间离散为一定数量的切比雪夫配点，在这些离散的配点处优化控制变量以减小计算负担；其次，将角速度作为控制变量，运用曲线拟合求解出角速度与时间的函数，经过积分求出无人机的角度、位置与时间的函数；再次,将结果代入粒子群优化模型并结合无人机运动学模型进行优化求解，根据分配的时间计算出最终的角速度、角度以及位置坐标；最后，在复杂环境下进行无人机轨迹规划仿真，通过与已有方法的对比，验证所提求解方法的有效性和可行性。结果表明，所提出的轨迹求解方法可以求出包括位置在内的各个运动学参数，规划出光滑的轨迹并且成功避开前进过程中的障碍物。所提方法有效提升了轨迹规划的求解维度，对实现智能自主化飞行有一定的参考价值。

Fixed time multi-constraint UAV trajectory planning based on particle swarm optimization

In view of the disadvantage that UAV is usually regarded as a particle in trajectory planning with particle swarm optimization algorithm,the flight time,angle and other parameters of UAV are ignored,a trajectory planning method combining numerical method with particle swarm optimization algorithm was proposed.Firstly,considering that the optimization of each control variable will cost a lot of time,the flight time of UAV was discretized as a certain number of Chebyshev points,and control variables were optimized at these discrete collocations to reduce the computational burden.Secondly,the angular velocity was taken as the control variable,the function of angular velocity and time was solved by curve fitting,and the function of angular and position of UAV with time was obtained by integration.Thirdly,the angular velocity,angle and position were calculated according to the allocated time.Finally,the simulation of UAV trajectory planning in complex environment was carried out,and the effectiveness and feasibility of the proposed method were verified by comparing the Monte-Carlo simulation results with the results of existing methods.The results show that the proposed method can calculate the kinematic parameters including position,plan a smooth trajectory and successfully avoid the obstacles in the process of forward.Therefore,the combination of particle swarm optimization algorithm and numerical method can solve the kinematics parameters of UAV and provide certain reference value for the improvement of the solution dimension of trajectory planning and the realization of intelligent autonomous flight.