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针对常规路径规划单纯追求路径最短、路径规划不灵活和实现复杂的缺点,提出了一种改进的移动机器人全局路径规划方法.该方法综合人工势场(APF)与粒子群优化算法(PSO)的优点,利用障碍物的排斥力生成路径的危险度地图(DDM),将路径长度与危险度的加权和作为PSO的适应度函数,获得了一条全局最优路径.该方法具有3个优点:粒子初始化及更新过程中会自动避开有障碍物的危险区域,规划出一条既安全相对长度又较短的路径;通过调整加权因子平衡长度与危险度在适应度函数中的比重,路径规划灵活;算法实现简单,收敛速度快,能满足移动机器人实时路径规划的要求.仿真结果证明了该算法的可行性和有效性.
Aiming at the shortcoming of the shortest path, the inflexible path planning and the complicated realization of conventional path planning, an improved global path planning method for mobile robot is proposed. This method combines artificial edge potential (APF) and particle swarm optimization (PSO) The advantage of this method is that a hazard map (DDM) is generated using the repulsive force of obstacles and a weighted optimal sum of path length and risk is taken as a fitness function of PSO to obtain a global optimal path.This method has three advantages: particle During the process of initialization and updating, a dangerous area with obstacles can be automatically avoided and a route with relatively short and relatively safe length can be planned. The path planning is flexible by adjusting the proportion of the balance weight and the degree of risk in the fitness function. The algorithm is simple in implementation and fast in convergence, which can meet the real-time path planning requirements of mobile robots.The simulation results show the feasibility and effectiveness of the proposed algorithm.