解决路径规划局部极小问题的角度累积法
作者:
作者单位:

1.南通大学;2.南京航空航天大学

作者简介:

通讯作者:

中图分类号:

TP242

基金项目:

国家自然科学基金项目(面上项目,重点项目,重大项目)


Angle Accumulation Method for solving Local Minimum Problem in Path Planning
Author:
Affiliation:

1.Nantong University;2.Nanjing University of Aeronautics and Astronautics

Fund Project:

The National Natural Science Foundation of China (General Program, Key Program, Major Research Plan)

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    摘要:

    通过势场法进行路径规划而导致的局部极小问题已经备受关注.针对该问题,本文分析了机器人陷入局部极小时势力场与速度方向间的关系,提出了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,分析了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,实现了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,提高了状态变换决策的灵活性与可靠性;与已有算法相比,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验证了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.

    Abstract:

    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 was analyzed when the robot has been trapped in the local minimum, and an obstacle avoidance method based on the motion cumulative angle was proposed. The internal and external turning angles of robot were taken to appraise the relationship between the direction and angle of the robot. Based on the angle cumulative, the "key reset point" was defined. By refreshing its own position and resetting its cumulative angle, the complex environment was simplified and the path planning was realized in an unknown environment. Simulation studies indicated that proper switching conditions for each state were 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 for shorter paln path and higher efficiency. The feasibility of this method was verified in path planning experiment by the self-made mobile robot. These results showed that the proposed algorithm was suitable for the first time through the unknown complex environment and the scenario of path planning without mapping.

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  • 收稿日期:2021-01-23
  • 最后修改日期:2021-05-11
  • 录用日期:2021-05-12
  • 在线发布日期: 2021-07-01
  • 出版日期: