基于改进蚁群算法的移动机器人动态路径规划

Dynamic path planning of mobile robot based on improved ant colony algorithm

针对动态环境下的移动机器人最优路径问题,利用栅格法建模,提出1种改进蚁群算法。通过调整信息素启发因子和期望启发因子,自适应改变挥发系数。在路径规划时,提出相应的动态路径规划避障策略,使机器人在避障的同时得到最优或次优路径。实验结果表明,当机器人陷入凹型障碍并且在复杂环境搜索效率低的情况下,该文算法经过25代收敛找到最短路径;改进算法比基本蚁群算法进化代数减少近50代,同时能有效避免移动机器人和动态障碍物碰撞,并且获得15.656的无碰路径。

Aiming at the shortest path searching problem of mobile robots in a dynamic environment,a path planning model is established using the grid method,and an improved ant colony algorithm is presented.The volatilization coefficient is changed adaptively by adjusting pheromone heuristic factors and expectation heuristic factors.In the path planning process,a corresponding dynamic path planning collision avoidance strategy is proposed to enable the robot to obtain the optimal or sub-optimal path and avoid obstacle simultaneously.The experimental results show that the improved algorithm needs 25 generations to achieve better convergence and find the shortest path when the robot is trapped in a concave obstacle with low search efficiency in a complex environment;the improved algorithm reduces by nearly 50 generations than the basic ant colony algorithm,and it can effectively avoid the collision between the mobile robot and dynamic obstacles,and obtain a 15.656 collision-free optimal path.