Abstract:

A fuzzy potential field method based on virtual target point and Finite State Machine（FSM）?is proposed to solve the problem of local minimum point and over-long planned path existed in the traditional artificial potential field method for local path planning of the mobile robot. The virtual target point method based on artificial potential field is constructed to solve the local minimum problem, and it is set in an appropriate position to make the robot escape from the local minimum point area. The virtual target point method is combined with fuzzy control to predict the obstacle environment and avoid obstacles in time, so as to solve the problem of over-long path when the robot adopts?the virtual target point method to plan the path in complex environment. A model of the FSM is designed to judge the obstacle environment, and the algorithm conversion strategy is implemented to make the improved algorithm suitable for various environments. The designed method is tested on the MATLAB platform. The simulation results show that the algorithm can make the robot escape from the local minimum point and shorten the planned path. It is not only applicable to the simple and discrete environment, but also can plan the feasible optimization path in the complex environment where the traditional algorithm is difficult to operate, such as the wall-like, U-shaped and multiple U-shaped obstacles environments.

Key words: mobile robot, local path planning, fuzzy potential field method, virtual target point, local minimum point, Finite State Machine（FSM）

摘要：

针对采用传统人工势场法进行移动机器人局部路径规划时存在的局部极小点和规划路径过长等问题，提出了一种基于虚拟目标点和有限状态机的模糊势场法。构造基于人工势场的虚拟目标点法来解决局部极小点问题，在合适的位置设置虚拟目标点使机器人逃离局部极小点区域。将虚拟目标点法与模糊控制相结合，对障碍物环境进行预测，及时避障，解决机器人在复杂环境中采用虚拟目标点法规划路径时存在的路径过长问题。设计一个有限状态机来判断障碍物环境，执行算法转换策略，使改进算法适用于多种复杂环境。所设计算法在MATLAB平台上进行了仿真验证。结果表明，该算法能够使机器人逃出局部极小点、缩短规划路径。算法不仅适用于简单、离散环境，在传统算法运行困难的、复杂的环境中，例如墙型、U型和多U型障碍物环境，也能规划出可行的优化路径。

关键词: 移动机器人, 局部路径规划, 模糊势场法, 虚拟目标点, 局部极小点, 有限状态机