基于凸优化粒子群算法的在线航迹规划

Online Route Planning Based on Particle Swarm Optimization with Convex Optimi⁃zation

针对未知环境中无人机可视图有限的路径规划问题,提出了一种基于凸优化的粒子群算法(Particle swarm optimization,PSO)进行路径点选取。在迭代寻优过程中以凸优化求解出的轨迹、避障以及到达终点距离等为元素设计粒子群的适应度函数,在获得最优路径点后再将路径点之间的轨迹显示出来。将所得轨迹作为同时定位与地图创建(Simultaneous localization and mapping,SLAM)的一部分来建立更加可信的环境地图。理论分析和实验仿真结果表明,与其他智能算法以及基于采样的路径规划算法相比,基于凸优化的粒子群算法可以有效地提高路径规划的效率以及减少规划路径的长度。

Aiming at the path planning problem of unmanned aerial vehicle(UAV)with limited view ability in unknown environment,a particle swarm optimization(PSO)algorithm based on convex optimization is proposed to select path points.In the iterative optimization process,the fitness function of particle swarm is designed based on the trajectory,obstacle avoidance and the distance to the end point solved by the convex optimization.The trajectory between the path points is displayed after the optimal path point is obtained.The obtained trajectory is used as a part of simultaneous localization and mapping(SLAM)to build a more reliable environment map.Theoretical analysis and experimental simulation results show that compared with other intelligent algorithms and sample-based path planning algorithms,the proposed PSO based on convex optimization can effectively improve the efficiency of path planning and reduce the length of the planned path.