一种改进PSO的室内机器人路径规划方法

Path Planning Method for Indoor Robot Based on Improved PSO

摘要:

针对室内空间局限性造成的移动机器人路径规划难度提升问题,文章分析了机器人室内移动中转弯、启停等运动特征,为获得最优规划路径引入了粒子群算法(particle swarm optimization, PSO),同时为改善经典算法中收敛度低,易早熟等问题,首先使用收敛因子、线性递减、非线性凹函数、随机分布方式等对PSO惯性权重的选取进行了讨论,并结合三次样条插值方法、选取罚函数作为适应度函数等对PSO进行了算法改进,最后,以实验室作为室内环境背景进行了仿真实验,并与经典的PSO路径规划方法进行了对比,实验结果表明,文章中改进的PSO路径规划方法精度高于经典PSO方法5%,平均寻优时间比经典PSO的少5s左右,能够有效的提高规划路径的平滑度,对于室内环境中机器人路径规划具有良好的实时性和有效性。

Abstract:

Aiming at the difficulty of path planning for mobile robots caused by the limitation of indoor space, this paper analyses the characteristics of turning, starting and stopping in indoor mobile robots, and introduces particle swarm optimization (particle swarm optimization, PSO) to obtain the optimal path planning. At the same time, in order to improve the problems of low convergence and early maturity in classical algorithms, convergence factor, linear decline and non-linear concave are first used. The selection of inertia weight of PSO is discussed in terms of function and random distribution. The algorithm of PSO is improved by using cubic spline interpolation method and penalty function as fitness function. Finally, the simulation experiment is carried out with laboratory as indoor environment background, and compared with the classical PSO path planning method. The experimental results show that the improved PSO path in this paper is better than the classical PSO path planning method. The accuracy of path planning method is 5% higher than that of classical PSO method, and the average optimization time is about 5 seconds less than that of classical PSO. It can effectively improve the smoothness of path planning, and has good real-time and effectiveness for robot path planning in indoor environment.