基于人工势场法的机器人路径规划改进方法的研究

Improved Robot Path Planning Method Based on Artificial Potential Field Method

随着各种自主导航机器人的发展,全局路径规划算法和局部路径规划算法也得到广泛应用,人工势场法是机器人局部路线设计方法中最为简单、最可行的一个。传统的人工势场法在路线规划过程中易陷入局部极小点的陷阱区域和目标无法达到的问题,为此,提供了一定的改善办法。考虑到障碍物离目标太近可能导致目标无法触及的问题,提出在斥力公式中增加目标点与机器人的相对距离,从而使得目标点成为机器人工作环境中势能的最低点的思想,通过重新确定斥力公式的方法来实现。同时,提出添加中间点的思路,解决局部极小值带来的陷阱问题。当机器人被局部极小值困住时,会创建一个中间目的地点,引导机器人避开陷阱,使机器人轻松到达目的地点。最后用MATLAB模拟实验仿真实验。首先证明重新定义的斥力公式以及增加中间目标点在机器人导航中的可行性,其次将所提的改进方法与传统的人工势场法相比,实验表明该改进方法避免了陷入目标不可达和局部极小点的问题,证明了所提改进方法的优越性。

With the development of various autonomous navigation robots,global path planning algorithms and local path planning algorithms are also widely used.The artificial potential field method is the simplest and most effective of the robotic local path planning algorithms.However,traditional artificial potential field methods easily fall into the trap of local minimum points and unattainable goal problems in the path planning process.So,the corresponding improvement methods were proposed.For the problem that the obstacle is too close to the target point,the relative distance between the obstacle and the robot was increased in the repulsion formula,so that the target point became the lowest point of potential energy in the robot’s working environment,which was realized by redefining the repulsive force formula;the idea of adding intermediate target points was proposed to solve the trap problem caused by local minima.When the robot fell into the trap of local minima,an intermediate target point was generated to guide the robot to escape from the trap,so that the robot could reach the target point smoothly.Finally,MATLAB was used for simulation experiment.The simulation experiment first proves the feasibility of the redefinition of the repulsion formula and the increase of the intermediate target point in the robot navigation.Then,compared with the traditional artificial potential field method,the proposed improvement method can avoid the trap of falling into the target unreachable and local minimum point,which proves the superiority of the proposed improved method.