基于改进势场蚁群算法的机器人路径规划

提出一种全局静态环境下移动机器人路径规划的改进势场蚁群算法.该算法采用人工势场法求得的初始路径和机器人与下一个节点之间的距离综合构造启发信息,并引入启发信息递减系数,避免了传统蚁群算法由于启发信息误导所致的局部最优问题;依据零点定理, 提出初始信息素不均衡分配原则,不同的栅格位置赋予不同的初始信息素,降低蚁群搜索的盲目性,提高算法的搜索效率;设定迭代阈值,自适应调节信息素挥发系数,使得该算法具有较高的全局搜索能力,避免出现停滞现象.仿真结果验证了所提出算法的可行性和有效性.

Robot path planning based on improved ant colony algorithm with potential field heuristic

The paper proposes an improved ant colony algorithm with potential field heuristic for the path planning of mobile robots in the global static environment. The algorithm constructs the comprehensive heuristic information based on the initial path obatined by using the artificial potential field method and the distance between the robot and the next node. Then, the heuristic information decline coefficient is introduced to avoid the local optimization problem caused by misleading information of the traditional ant colony algorithm. Based on the zero point theorem, this paper proposes an initial pheromone unequal allocation principle. Various grid positions are endowed with different initial pheromones, which decreases the blindness of ant colony search and improves the searching efficiency of the algorithm. An iterative threshold is set to adaptively adjust pheromone volatilization coefficients. In this way, the algorithm has excellent global searching ability, and the stagnation phenomenon can be avoided. The simulation results show the feasibility and effectiveness of the proposed method.