一种未知环境下的移动机器人路径规划方法

Path Planning Method for Mobile Robots in Unknown Environment

针对同时存在全局与局部环境的移动机器人路径规划问题,提出了一种分层路径规划方法.采用改进势场-蚁群融合算法进行规划.首先,在全局环境下采用改进蚁群算法进行路径规划,为了解决蚂蚁在搜索过程易陷入“死锁”的缺陷,提出屏蔽U型陷阱措施;在启发函数中加入目标点信息,使得目标点在整个规划过程对蚁群有引导作用,能够避免蚁群陷入局部最优,同时提高了算法的收敛速度;引进最大最小蚂蚁系统思想限定信息素范围,防止因信息素变化过大造成收敛速度慢或因过快而出现局部最优解.其次,在遭遇运动障碍物时,调用人工势场算法进行局部避障.最后,使用中点替代策略对输出的最优路径进行拐角优化,有效地平滑了路径尖峰,减少了拐角个数.为了体现所提方法的优势,实验在多U型环境下进行,仿真结果验证了算法的有效性和可行性.

Aiming at the path planning problem of mobile robot with both global and local environment,a hierarchical path planning method is proposed.The improved potential field-ant colony fusion algorithm is used for path planning.Firstly,the improved ant colony algorithm is used for path planning in the global environment.In order to solving the defect that ants fall into deadlock easily in search process,U trap shielding measures are proposed.Adding target point information to the heuristic function makes the target point guide the ant colony in the whole planning process,which can avoid the ant colony from falling into the local optimum and improve the convergence speed of the algorithm.The idea of maximum and minimum ant system is introduced to limit the range of pheromone to prevent the slow convergence rate or local optimal solution due to too fast pheromone change.Secondly,when encountering dynamic obstacles,the artificial potential field algorithm is called for local obstacle avoidance.Finally,the midpoint substitution strategy is used to optimize the corners of the optimal output path,which effectively smoothes the corners of the path and reduces the number of corners.In order to reflect the advantages of the proposed method,the experiment was conducted in a multi-U environment,and the simulation results verified the effectiveness and feasibility of the algorithm.