改进人工势场法的移动机器人避障轨迹研究

doi:10.3778/j.issn.1002-8331.2108-0122

摘要/Abstract

摘要： 针对传统人工势场法在多障碍物复杂环境的全局路径规划中出现的目标不可达、易陷入陷阱区域以及局部极小点问题，提出一种简化障碍物预测碰撞人工势场法（simplified obstacles and predict collision of artificial potential field method，SOPC-APF），算法引入预测碰撞思想，在机器人未进入陷阱区域或者极小点问题前做出决策；对于多障碍物的斥力与目标点的引力产生的合力使机器人陷入震荡，提出简化障碍物，即简化为影响范围内目标点一侧的受限障碍物；针对目标不可达问题，在碰撞预测基础上，设定虚拟目标点，经改进的斥力函数引导机器人快速生成一条平滑、平稳、无碰撞的路径。通过与传统算法、改进APF算法以及改进蚁群算法的仿真对比实验表明，SOPC-APF有效解决了人工势场法不适用于多障碍物复杂环境的问题，以及传统算法容易陷入陷阱区域和局部极小点问题。

关键词: 预测碰撞, 简化受限障碍物, 虚拟目标点, 人工势场法

Abstract: Aiming at the problem that method of the tradition artificial potential field in the global path planning will result in inaccessibility of target, easy to fail into trap area and local minimum. A simplified obstacles and predict collision of artificial potential field method（SOPC-APF） is proposed. The concept of collision prediction is introduced that robot makes decisions before not entering trap area and local minimum. Because of the combined force of repulsion generated by multiple obstacles and the attraction of target causes the robot to fail into oscillation, simplified obstacles are proposed that means at side of the target within the influence range as restricted obstacles. Virtual target is set based on collision prediction for the problem of inaccessibility of target, and robot is guided to fast generate a smooth, stable and collision-free path by improved repulsive force function. Compared with traditional algorithm, improved APF algorithm and improved ant colony optimization algorithm, simulation experiments demonstrate that SOPC-APF can effectively solve the problem that APF is not suitable for multi-obstacle complex environment, and the traditional APF is easy to fail into trap area and local minimum.

Key words: collision prediction, simplified restricted obstacles, virtual target, artificial potential field method

李二超, 王玉华. 改进人工势场法的移动机器人避障轨迹研究[J]. 计算机工程与应用, 2022, 58(6): 296-304.

LI Erchao, WANG Yuhua. Research on Obstacle Avoidance Trajectory of Mobile Robot Based on Improved Artificial Potential Field[J]. Computer Engineering and Applications, 2022, 58(6): 296-304.

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