未知环境下改进的基于BUG算法的移动机器人路径规划

Improved Path Planning Based on Bug Algorithm for Mobile Robot in Unknown Environment

对未知环境下移动机器人路径规划的方法和发展现状进行了概述总结,指出了各种方法的优点和不足。同时研究了环境未知情况下的移动机器人实时路径规划问题,将Bug算法与基于滚动窗口的路径规划相结合,提出了一种改进的移动机器人路径规划方法。规划时只考虑当前状态下所必须的传感数据,不必计算障碍物的边线解析式,节省了存储空间,提高了规划效率,保证了算法的实时性。算法采用两种行为模式,为了保证路径规划的完备性和全局收敛,同时给出了全局收敛标准。由于算法的固有原理,在陷阱区域移动机器人容易左右徘徊,无法达到目标点。针对算法的这个问题,利用虚拟障碍的概念提出了基于局部切线图算法的移动机器人路径规划。最后对本算法的收敛性和完备性给予了证明。仿真实验验证了该方法的有效性。

The current research situation of path planning for mobile robot in unknown environment was summarized In addition, the advantages and disadvantages of these algorithms were pointed out. The mobile robot path planning in unknown environment was studied. Bug algorithm and rolling path planning were combined in the planning, so an improved path planning was proposed. Only the necessary sensing data instead of the analytical expression of the obstacle were calculated in planning so as to save memory and improve the planning efficiency, thus the planning in real-time was guaranteed. The algorithm used the two basic behaviors of motion. In order to maintain the planner＇s completeness, a global criterion was added in order to guarantee convergence to the goal. As one of the innate limitations of the principle of algorithm, the robot tended to be in a trap situations due to local minima. In order to solve this problem, an path planning was proposed based on local tangent graph algorithm for mobile robot utilizing the concept of virtual obstacle. At last, the proof of convergence and completeness was given. The effectiveness of this method was verified by simulation results.