完全未知环境下机器人探索路径策略与仿真

为了使工作在完全未知环境中的机器人能够灵活应对多种类型障碍,提出了一种完全未知环境中机器人探索路径的新思路,即充分利用机器人感知区域内的局部环境信息,在BUG路径规划算法基础上采用所提出的两层路径选择策略(TLPC)以实时决策机器人的行为.在两层路径选择策略的作用下,一方面使机器人在面对障碍时具有类人的路径选择功能,避免了机器人避障的盲目性;另一方面由于机器人具有逆转跟踪障碍边界方向的功能,在面对特殊障碍时能够以较短路径绕行,从而减小整体路径的冗余度.此算法体现了机器人在未知环境中的智能性和对较复杂环境的适应性.仿真实验证明算法可行性.

Path Exploring Strategy and Simulation for Robot in Full Unknown Environment

A new online path planning method is presented for full unknown environment. The BUG algorithm and the two-level strategies of path choice presented are combined to decide robot's action real-timely. In the method two-level strategies of path choice simulate human's thinking mode in order to decide which direction of obstacle robot should follow. It can reduce the blindness of robot in avoiding obstacles and path redundancy, also the planning algorithm makes the robot adapt more complex environment. At last, simulation result indicates the feasibility of the algorithm.