Yang Xiaoyu, Moallem Mehrdad, Patel Rajni V
Department of Electrical and Computer Engineering, University of Western Ontario, Canada.
IEEE Trans Syst Man Cybern B Cybern. 2005 Dec;35(6):1214-24. doi: 10.1109/tsmcb.2005.850177.
Most conventional motion planning algorithms that are based on the model of the environment cannot perform well when dealing with the navigation problem for real-world mobile robots where the environment is unknown and can change dynamically. In this paper, a layered goal-oriented motion planning strategy using fuzzy logic is developed for a mobile robot navigating in an unknown environment. The information about the global goal and the long-range sensory data are used by the first layer of the planner to produce an intermediate goal, referred to as the way-point, that gives a favorable direction in terms of seeking the goal within the detected area. The second layer of the planner takes this way-point as a subgoal and, using short-range sensory data, guides the robot to reach the subgoal while avoiding collisions. The resulting path, connecting an initial point to a goal position, is similar to the path produced by the visibility graph motion planning method, but in this approach there is no assumption about the environment. Due to its simplicity and capability for real-time implementation, fuzzy logic has been used for the proposed motion planning strategy. The resulting navigation system is implemented on a real mobile robot, Koala, and tested in various environments. Experimental results are presented which demonstrate the effectiveness of the proposed fuzzy navigation system.
大多数基于环境模型的传统运动规划算法在处理现实世界移动机器人的导航问题时表现不佳，因为现实环境是未知的且可能动态变化。本文针对在未知环境中导航的移动机器人，开发了一种使用模糊逻辑的分层目标导向运动规划策略。规划器的第一层利用全局目标信息和远程传感数据生成一个中间目标，即路径点，该路径点在检测区域内朝着目标方向提供了一个有利方向。规划器的第二层将此路径点作为子目标，并利用短程传感数据引导机器人到达子目标，同时避免碰撞。连接初始点到目标位置的最终路径类似于可见性图运动规划方法生成的路径，但在这种方法中不做关于环境的假设。由于其简单性和实时实现能力，模糊逻辑被用于所提出的运动规划策略。最终的导航系统在实际移动机器人Koala上实现，并在各种环境中进行了测试。给出的实验结果证明了所提出的模糊导航系统的有效性。
