基于改进粒子群算法的移动机器人路径规划

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。

Path planning of mobile robot based on improved 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.