基于改进蚁群算法的机器人路径规划问题研究

Research on Robot Path Planning Based on Improved Ant Colony Algorithm

针对基本蚁群算法在处理障碍环境下机器人路径规划问题时表现出的多样性不足及寻优能力弱等缺陷,提出自适应蚁群算法,通过引入自适应信息素挥发系数,动态地改变算法在迭代不同时期的算法多样性,在迭代前期提高寻优能力;在迭代中后期,提高算法收敛速度。通过对基本蚁群算法和改进蚁群算法的仿真结果分析可知,改进后算法的最优路径长度得到有效降低,收敛速度更快,获得一条无碰撞的路径,保证了机器人路径的安全性,提高了算法的多样性及寻优能力。

Aiming at the defects of the basic ant colony algorithm such as insufficient diversity and weak optimization ability when dealing with robot path planning problems in obstacle environment,an adaptive ant colony algorithm is proposed.By introducing the adaptive pheromone volatilization coefficient,the algorithm diversity in different iteration stages is dynamically changed,and the optimization ability is improved in the early iteration stage.In the middle and later stages of iteration,the convergence speed of the algorithm is improved.Through the analysis of the simulation results of the basic ant colony algorithm and the improved ant colony algorithm,it can be seen that the optimal path length of the improved algorithm is effectively reduced,the convergence speed is faster,a collision-free path is obtained,the safety of the robot path is ensured,and the diversity and optimization ability of the algorithm are improved.