基于改进蚁群算法的盘点型机器人路径规划

Path Planning of Inventory Robot Based on Improved Ant Colony Algorithm

针对盘点型机器人盘点物品过程中路径规划实时性和稳定性差的问题,以传统的蚁群算法为基础,提出了一种改进型的蚁群算法。改进型的蚁群算法包括三点优化:第一是提出了自适应的挥发系数设置方法,即算法前期设置较小的挥发系数,减小蚂蚁间互相吸引;算法后期,挥发系数设置较大,提高算法收敛速度。第二是对各个路径上的初始浓度做出调整,加大了起始点和终点连线附近的信息素浓度。这样能较大提高前期搜索的速度。第三是全局信息素更新时,按单次迭代出的路径长短,在较短路径上加强信息素浓度,在较长路径上削减信息素浓度。研究结果表明,当对传统算法做出这三点优化后,改进后的算法不仅路径规划收敛的速度更快,效率更高,而且寻得的路径也更优更稳定。经仿真实验验证,在类旅行商问题上,改进后的算法确实有更快的收敛速度,且能避免陷入局部最优解,而得到全局最优路线。

Aiming at the problem of poor real-time and stability of path planning in inventory process of inventory robots,we propose an improved ant colony algorithm based on traditional ant colony algorithm.It includes three-point optimization.The first is to propose an adaptive volatilization coefficient setting method which sets a small volatilization coefficient in the early stage to reduce mutual attraction between the ants,and in the later stage,sets a large volatilization coefficient to improve the convergence speed.The second is to adjust the initial concentration on each path to increase the pheromone concentration near the start and end points.This can greatly improve the speed of the prophase search.The third is the global pheromone update,the length of the path by a single iteration,the pheromone concentration is enhanced on the shorter path,and the pheromone concentration is reduced on the longer path.The research shows that when these three optimizations are made to the traditional algorithm,the improved algorithm not only converges faster and more efficiently,but also finds better and more stable paths.The simulation experiment proves that the improved algorithm has a faster convergence speed on the traveling salesman problem,and can avoid falling into the local optimal solution and get the global optimal route.