广东工业大学学报 ›› 2022, Vol. 39 ›› Issue (05): 93-101.doi: 10.12052/gdutxb.220074

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模型预测控制下多移动机器人的跟踪与避障

彭积广, 肖涵臻   

  1. 广东工业大学 自动化学院，广东 广州 510006
  • 收稿日期:2022-04-02 发布日期:2022-07-18
  • 通信作者: 肖涵臻(1991–)，男，讲师，博士，硕士生导师，主要研究方向为智能移动机器人控制、多智能体编队，E-mail：xiaohanzhen77@foxmail.com
  • 作者简介:彭积广(1996–)，男，硕士研究生，主要研究方向为多移动机器人协同控制
  • 基金资助:
    国家自然科学基金青年基金资助项目(62003092)

Tracking and Obstacle Avoidance of Multi-mobile Robots Under Model Predictive Control

Peng Ji-guang, Xiao Han-zhen   

  1. School of Automation, Guangdong University of Technology, Guangzhou 510006, China
  • Received:2022-04-02 Published:2022-07-18

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摘要： 提出了一种基于距离和速度的机器人之间的避障方法，通过与机器人避开障碍物的人工势场法相结合，建立一致性控制编队控制协议。首先，建立机器人之间的通信拓扑关系，以便机器人之间的信息交流。在编队控制层面上，设计具有避碰的编队控制律。然后，在编队跟踪层面上，运用模型预测控制方法，将编队误差运动问题按代价函数转化为最小优化问题。为了在线高效地求解该优化问题，运用了一种广义投影神经网络优化的方法，以便最优解作为控制输入。最后，对多移动机器人编队进行了仿真，验证了所提出策略的有效性。

关键词: 人工势场法, 模型预测控制, 多机器人编队控制, 广义投影神经网络

Abstract: Aiming to control the formation tracking and obstacle avoidance system of multi-mobile robots under the changing topology, an obstacle avoidance method based on distance and speed between robots and an artificial potential field method to avoid obstacles are proposed to establish a consistent control formation control protocol. Firstly, the communication topology between robots is established to facilitate the information exchange between robots. At the level of formation control, a formation control law with collision avoidance is designed. Then, at the level of formation tracking, the formation error motion problem is transformed into a minimum optimization problem according to the cost function by using model predictive control method. In order to efficiently solve the optimization problem online, a generalized projection neural network optimization method is used, in which the optimal solution is used as the control input. Finally, the simulation of multi-mobile robot formation verifies the effectiveness of the proposed strategy.

Key words: artificial potential field method, model predictive control (MPC), multi-robot formation control, general projection neural network (GPNN).

中图分类号: 

  • TG156
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