基于改进麻雀搜索算法的移动机器人路径规划

Path Planning of Mobile Robot Based on Improved Sparrow Search Algorithm

摘要: 针对移动机器人在不同工作环境中，收敛速度慢、易陷入局部最优值、非必要拐点多等问题，本文提出一种改进麻雀搜索算法的路径规划策略。首先，在种群初始阶段采用K-means聚类方法对麻雀个体位置进行聚类和区分，提高种群的工作效率，消除随机性的影响；其次，在危险感知转移策略中，构造一个基于正余弦函数的自适应更新表达式，避免达到局部最优；最后，提出一种拐点系统评估机制，将路径转角处的折线变换为平滑的直线，使机器人更快地到达目标。仿真和实验结果表明，所提改进算法的可靠性和优越性。

Abstract: Aiming at the problems of slow convergence speed, easy to fall into local optimization, and large number of path inflection points in different working environments of mobile robots, an improved sparrow search algorithm is proposed. Firstly, the K-means clustering method is used to cluster and distinguish the individual positions of sparrows in the initial stage of population, which accelerates the work efficiency of the population and eliminates the influence of randomness. Then, in the dan-ger perception transfer strategy, an adaptive position update formula based on the sine and cosine function is constructed to avoid falling into local optimality; Finally, an inflection point system eval-uation mechanism is proposed, which transforms the polyline at the corner of the path into a smooth straight line, so as to the robot can reach the target faster. Experimental results show that the optimization efficiency of the proposed improved algorithm is significantly improved, and the path length and inflection point number are significantly reduced compared with the ant colony al-gorithm, the improved ant colony algorithm and the adaptive sparrow algorithm.