基于改进多步长蚁群算法的机器人路径规划

针对移动机器人路径规划时蚁群算法稳定性不足且易陷入局部极值的问题，提出一种改进多步长蚁群算法,改进了基本蚁群算法中只能单步位移且方向固定的移动模式，从而减少路径的转向次数且缩短路径长度。针对移动机器人实际工作时可能遇到的环境坡度大的情形，在启发函数中加入高度因素，提升算法环境适应能力。采用自适应挥发机制，加快算法收敛速度及效率。结果表明：在具有变化高度的环境中，与基本蚁群算法相比，改进多步长蚁群算法规划出的路径更平缓且距离更短，更适合移动机器人工作情景。

Path Planning of Robot Based on Improved Multi-step Ant Colony Algorithm

To settle the problem of insufficient stability and easy to fall into local extreme values in mobile robot path planning by using ant colony algorithm,an improved multi-step ant colony algorithm was proposed.The movement mode in the basic ant colony algorithm which could only move in one step with a fixed direction was improved,so the turning times and the path distance were reduced.For the situation that the robot might encounter a large slope in actual work,the height factor was added into the heuristic function to improve the adaptive capacity of the algorithm to the environment.Adaptive volatilization mechanism was adopted to accelerate the convergence speed and efficiency of the algorithm.The results show that compared with the basic ant colony algorithm,in the environment with variable hight,the path planned by using the improved multi-step ant colony algorithm is more gentler and shorter,so the designed method is more suitable for the actual operating environment of robot.