东北大学学报(自然科学版) ›› 2022, Vol. 43 ›› Issue (9): 1225-1233.DOI: 10.12068/j.issn.1005-3026.2022.09.002

• 信息与控制 • 上一篇    下一篇

基于障碍物可达区域预测的机器人实时避障算法

彭帆, 谢永芳, 陈晓方, 殷泽阳   

  1. (中南大学 自动化学院, 湖南 长沙410083)
  • 发布日期:2022-09-16
  • 通讯作者: 彭帆
  • 作者简介:彭帆(1997-),男,湖南长沙人,中南大学硕士研究生; 谢永芳(1972-),男,河南周口人,中南大学教授,博士生导师; 陈晓方(1975-),男,福建福州人,中南大学教授,博士生导师.
  • 基金资助:
    国家自然科学基金资助项目(62103446, 61725306).

Robot Real-time Obstacle Avoidance Algorithm Based on Prediction of Obstacle Reachable Area

PENG Fan, XIE Yong-fang, CHEN Xiao-fang, YIN Ze-yang   

  1. School of Automation, Central South University, Changsha 410083, China.
  • Published:2022-09-16
  • Contact: YIN Ze-yang
  • About author:-
  • Supported by:
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摘要: 针对复杂环境下的机器人路径规划与自主避障问题,提出了基于动态障碍物可达区域预测的实时避障算法.对静态障碍物进行描述和建模,建立动态障碍物状态更新预测方程,实现对动态障碍物质心可达区域的预测.分别面向动态、静态障碍物提出基于可达区域预测的多步椭圆包络势场和基于新型Sigmoid函数的势场,修正目标的对数Lyapunov引力场,给出多类型障碍物空间下的机器人实时避障算法.数值仿真和实验结果表明,与传统方法相比实时避障算法可使机器人避障过程中的路径长度更短、安全性更高及最大行驶角变化幅值更小.

关键词: 机器人;可达区域预测;多步椭圆包络势场;Sigmoid函数势场;实时避障

Abstract: For the path planning and autonomous obstacle avoidance problem of robot in complex environment, a real-time obstacle avoidance algorithm is proposed based on the prediction of the reachable area of dynamic obstacles. The static obstacles are described and modelled, and the state update prediction equations of dynamic obstacles are established to realize the prediction of the reachable area of dynamic obstacle mass center. For dynamic and static obstacles, a multi-step elliptic envelope potential field based on reachable area prediction and a potential field based on the novel Sigmoid function are proposed, respectively, to modify the target’s logarithmic Lyapunov gravitational field. A real-time obstacle avoidance algorithm is given for robot in the environment of multi-type obstacles. The numerical simulation and experimental results show that compared with traditional methods, the real-time obstacle avoidance algorithm can realize shorter path length, higher security and smaller driving angle during obstacle avoidance process.

Key words: robot; reachable area prediction; multi-step elliptic enveloping potential field; Sigmoid function potential field; real-time obstacle avoidance

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