基于改进蚁群算法的移动机器人路径规划研究

Research on path planning of mobile robot based on improved ant colony algorithm

针对传统蚁群算法在移动机器人路径规划中存在收敛速度慢、易陷入局部最优和规划路径不平滑等问题,提出一种用于移动机器人路径规划的改进蚁群算法。首先在状态转移概率中引入平滑函数,使蚂蚁在进行路径节点选择时,考虑路径的平滑性。然后在对路径信息素更新时,引入多目标评价函数;同时提出一种基于熵权的分段信息素更新方式,每次迭代规划路径按多目标评价函数数值进行排序并分段,对不同的分段,引入不同的信息素强度放大系数,提升了算法的收敛速度。最后对规划路径进行二次优化,即先对路径节点进行优化,减少不必要的转弯节点,减小了路径转弯角度以及路径长度;再利用贝塞尔曲线对节点优化后路径的转弯拐点处进行平滑。在20×20的简单和复杂栅格环境中进行仿真实验,结果表明,改进蚁群算法规划出的路径长度更短、转弯角度更小和路径更加平滑,同时改进蚁群算法的迭代收敛速度更快,验证了改进蚁群算法在移动机器人路径规划中的优越性。

Aiming at the problems of traditional ant colony algorithm in path planning of mobile robot,such as slow convergence speed,easy to fall into local optimal and unsmooth path planning,an improved ant colony algorithm for path planning of mobile robots was proposed.Firstly,a smoothing function was introduced into the state transition probability to make ants consider the smoothness of the path when selecting the path node.When updating the path pheromone,a multi-objective evaluation function was introduced,and a segmental pheromone update method based on entropy weight was proposed.Each iteration planning path was sorted and segmented according to the value of the multi-objective evaluation function.For different segments,different pheromone intensity amplification coefficients were introduced to improve the convergence speed of the algorithm.Finally,the secondary optimization of the planned path was carried out,that is,firstly,the path nodes were optimized to reduce unnecessary turning nodes and reduce the turning angle and path length of the path;then,the Bezier curve was used to smooth the turning inflection point of the path optimized by nodes again.Simulation experiments in simple and complex grid environments of 20×20 show that the path planned by the improved ant colony algorithm is shorter,the turning angle is smaller and the path is smoother.Meanwhile,the iterative convergence speed of the improved ant colony algorithm is faster,which verifies the superiority of the improved ant colony algorithm in path planning of mobile robot.