基于改进的蚁群算法的移动机器人路径规划

针对移动机器人路径规划中的传统蚁群算法收敛精度低、易陷入局部最优等问题,提出一种改进蚁群算法。首先,对算法的转移概率进行改进,加入转向代价,减少不必要的转折,并针对启发函数启发性能不够强,对路径启发信息进行改进;然后,提出一种自适应的参数调整伪随机状态转移策略,动态改变参数值,避免过早陷入搜索停滞,增强搜索的全面性,同时对信息素更新方式进行改进,调整信息素挥发系数,保持蚂蚁发现最优路径的能力;最后,通过Matlab与其他算法进行对比分析。仿真结果表明,改进的蚁群算法收敛速度快,且路径长度和算法迭代次数有明显减少,能得到全局最优路径。改进蚁群算法具有可行性、有效性,在移动机器人路径规划中有一定的应用价值。

Path planning of mobile robot based on improved ant colony algorithm

An improved ant colony algorithm is proposed for the traditional ant colony algorithm in mobile robot path planning with low convergence precision and easy to fall into local optimum.Firstly, the transition probability of the algorithm is improved, the steering cost is added, the unnecessary turning is reduced, and the heuristic performance of the heuristic function is not strong enough to improve the path heuristic information.Then an adaptive parameter adjustment pseudo-random state transition strategy is proposed to dynamically change the parameter values to avoid prematurely falling into search stagnation and enhance the comprehensiveness of the search. At the same time, the pheromone update method is improved, the pheromone volatilization coefficient is adjusted, and the ant is found to find the optimal path capability. Finally, through matlab and other algorithms, the simulation results show that the improved ant colony algorithm has a fast convergence speed, the path length and algorithm iteration number are significantly reduced, and the global optimal path can be obtained, Proving the feasibility and effectiveness of the improved ant colony algorithm, which has certain application value in mobile robot path planning.