改进人工势场法的移动机器人路径规划

Robot Path Planning Based on Improved Artificial Potential Field Method

针对传统人工势场法应用于移动机器人路径规划存在的局部极小等缺陷,提出了改进的人工势场模型:以四幅典型环境场景为例,分析了人工势场法中局部极小点问题的成因;引入斥力偏转模型,引导机器人在路径规划时避开局部极小点;引入斥力增益系数函数,进一步优化路径规划中航向改变过大的问题;建立规划路径评价模型,对提出的改进人工势场模型下路径规划的优劣给出量化评价。该模型通过修正斥力方向,减小了规划路径的长度以及曲度,降低了对机器人机动性能的要求。仿真结果表明,该模型在解决部分局部极小问题的同时,提高了规划路径的质量,较好解决了静态环境下机器人的路径规划问题。

When using traditional artificial potential field method for path planning of mobile robots, there are problems such as local minimum. In response to the problems, an artificial potential field improvement model is proposed. Taking four typical environmental scenarios as examples, the causes of the local minimum problem in the artificial potential field method are analyzed. Repulsive deflection model is introduced to guide the robot to avoid local minima during path planning. Repulsive gain coefficient function is introduced to further optimize the problem of excessive heading changes in path planning. Planning path evaluation model is established to give a quantitative assessment of the merits of path planning. The improved model significantly reduces the length and curvature of the planned path by correcting the direction of the repulsive force. Therefore, it reduces the requirements for robot maneuverability. The simulation results show that the model can improve the quality of the planning path while solving the local minimum problem of static path planning.