解决势场法路径规划中局部极小问题的角度累积法

Angle accumulation method for solving local minimum problem in path planning with potential field method

通过势场法进行路径规划而导致的局部极小问题备受关注.针对该问题,分析机器人陷入局部极小时势力场与速度方向间的关系,提出一种基于运动累积角的避障法.该方法利用机器人行进过程中自身内外角及其累计变化量,分析自身与目标间的方向角度关系,定义了基于角度累积量的“关键重置点”;通过自身位置刷新、累积角度重置,简化了复杂环境,实现在未知环境中的路径规划.仿真实验表明,该方法可实现状态间的转换条件的合理设计、状态间的有序过渡与平稳运行,提高状态变换决策的灵活性与可靠性;与已有相关算法相比,该方法具有规划路程更短、运行效率更高的优点.利用自制的移动机器人实验来验证该方法的避障可行性.上述结果表明:该方法可解决机器人路径规划中的局部最小问题,适用于初次通过未知复杂环境且无需建图的路径规划场景.

The local minimum problem caused by path planning of potential field has received much more attention.To cope with this issue,in this paper,the relationship between the potential force field and the velocity direction is analyzed when the robot has been trapped in the local minimum,and an obstacle avoidance method based on the motion cumulative angle is proposed.The internal and external turning angles of the robot are taken to appraise the relationship between the direction and angle of the robot.Based on the angle cumulative,the “key reset point”is defined.By refreshing its own position and resetting its cumulative angle,the complex environment is simplified and the path planning is realized in an unknown environment.Simulation studies indicate that proper switching conditions for each state are designed to guarantee the orders of the state switching and smooth operation,and therefore the robot can improve the flexibility and reliability of decision.Compared to some existing methods,the method has the advantages of shorter plan path and higher efficiency.The feasibility of this method is verified by a path planning experiment on the self-made mobile robot.These results show that the proposed algorithm is suitable for the first time through the unknown complex environment and the scenario of path planning without mapping.