基于改进粒子群算法的移动机器人路径规划
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摘要
由于惯性权重取值不合适和迭代后期粒子群体多样性下降,导致传统粒子算法在移动机器人路径规划研究过程中存在局部最优解问题。针对此问题提出了一种改进粒子群算法的移动机器人路径规划方法。首先建立机器人路径规划的栅格地图模型,在此基础上对传统的粒子群算法进行了改进。随后,引入了基于相似度概念的非线性动态惯性权值调整方法,从而使得粒子的更新速率能够适配寻优过程的各个阶段,并且通过引入免疫算法中的免疫信息调节机制,增加了粒子的多样性,增强了其摆脱局部最优值的能力。仿真结果表明,所提出的改进粒子群算法具有更高的最佳路径搜索能力,其综合性能显著优于传统的粒子群算法。
Due to the inappropriate value of inertia weight and the diversity of particle population decreases in late iteration, traditional particle algorithm in the process of mobile robot path planning easy falls into the local optimal solution problem. Aiming at this problem, a path planning method of mobile robot with improved particle swarm optimization is proposed. Firstly, a grid map model of robot path planning is established. On this basis, the traditional particle swarm optimization algorithm is improved. The weight dynamic adjustment method based on the concept of similarity is introduced to update the particle update rate with the various stages of the optimization process, by introducing the immune information regulation mechanism in the immune algorithm to increase the diversity of particles to enhance its ability to get rid of the local optimum. The simulation results show that the proposed method can obviously improve the searching ability of the best path and its comprehensive performance is better than the traditional particle swarm optimization.
Due to the inappropriate value of inertia weight and the diversity of particle population decreases in late iteration, traditional particle algorithm in the process of mobile robot path planning easy falls into the local optimal solution problem. Aiming at this problem, a path planning method of mobile robot with improved particle swarm optimization is proposed. Firstly, a grid map model of robot path planning is established. On this basis, the traditional particle swarm optimization algorithm is improved. The weight dynamic adjustment method based on the concept of similarity is introduced to update the particle update rate with the various stages of the optimization process, by introducing the immune information regulation mechanism in the immune algorithm to increase the diversity of particles to enhance its ability to get rid of the local optimum. The simulation results show that the proposed method can obviously improve the searching ability of the best path and its comprehensive performance is better than the traditional particle swarm optimization.
引文
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[12] 张毅,代恩灿,罗元.基于改进遗传算法的移动机器人路径规划[J].计算机测量与控制,2016,24(1):313-316.
[13] 禹素萍,郁晓慧,许武军,等.基于PSO算法路径规划的研究[J].微型机与应用,2015,34(4):17-19.
[14] 赵甜甜,王思明.基于改进PSO算法的移动机器人路径规划[J].传感器与微系统,2018,37(2):57-60.
[15] 蒲兴成,李俊杰,吴慧超,等.基于改进粒子群算法的移动机器人多目标点路径规划[J].智能系统学报,2017,12(3):301-309.
[16] DAS P K, BEHERA H S, PANIGRAHI B K. A hybridization of an improved particle swarm optimization and gravitational search algorithm for multi-robot path planning[J]. Swarm & Evolutionary Computation, 2016, 28:14-28.
[17] POLI R, KENNEDY J, BLACKWELL T. Particle swam optimization[J]. Swam Intelligence, 2007(1):22-57.
[2] 王红卫,马勇,谢勇,等.基于平滑A*算法的移动机器人路径规划[J].同济大学学报(自然科学版),2010,38(11):1647-1650,1655.
[3] 马飞,杨皞屾,顾青,等.基于改进A*算法的地下无人铲运机导航路径规划[J].农业机械学报,2015,46(7):303-309.
[4] 熊壬浩,刘羽.A*算法的改进及并行化[J].计算机应用,2015,35(7):1843-1848.
[5] 李擎,谢四江,童新海,等.一种用于车辆最短路径规划的自适应遗传算法及其与Dijkstra和A*算法的比较[J].北京科技大学学报,2006(11):1082-1086.
[6] 黄纬.基于平面图的改进Dijkstra算法研究[J].江苏大学学报(自然科学版),2003(6):70-72.
[7] 王树西,吴政学.改进的Dijkstra最短路径算法及其应用研究[J].计算机科学,2012,39(5):223-228.
[8] 董俊,黄传河.改进Dijkstra算法在GIS导航应用中最短路径搜索研究[J].计算机科学,2012,39(10):245-247,257.
[9] 魏冠伟,付梦印.基于神经网络的机器人路径规划算法[J].计算机仿真,2010,27(7):112-116.
[10] ALGABRI M, MATHKOUR H, RAMDANE H, et al. Comparative study of soft computing techniques for mobile robot navigation in an unknown environment[J]. Computers in Human Behavior, 2015, 50:42-56.
[11] PANDEY A, PARHI D R. Optimum path planning of mobile robot in unknown static and dynamic environments using Fuzzy-Wind Driven Optimization algorithm[J]. Defence Technology, 2017, 13(1):47-58.
[12] 张毅,代恩灿,罗元.基于改进遗传算法的移动机器人路径规划[J].计算机测量与控制,2016,24(1):313-316.
[13] 禹素萍,郁晓慧,许武军,等.基于PSO算法路径规划的研究[J].微型机与应用,2015,34(4):17-19.
[14] 赵甜甜,王思明.基于改进PSO算法的移动机器人路径规划[J].传感器与微系统,2018,37(2):57-60.
[15] 蒲兴成,李俊杰,吴慧超,等.基于改进粒子群算法的移动机器人多目标点路径规划[J].智能系统学报,2017,12(3):301-309.
[16] DAS P K, BEHERA H S, PANIGRAHI B K. A hybridization of an improved particle swarm optimization and gravitational search algorithm for multi-robot path planning[J]. Swarm & Evolutionary Computation, 2016, 28:14-28.
[17] POLI R, KENNEDY J, BLACKWELL T. Particle swam optimization[J]. Swam Intelligence, 2007(1):22-57.