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

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

更新时间：2026-05-27

- 模型预测控制下多移动机器人的跟踪与避障
- Tracking and Obstacle Avoidance of Multi-mobile Robots Under Model Predictive Control
- 广东工业大学学报 2022年39卷第5期页码：93-101
- 作者机构：
  
  广东工业大学自动化学院,广东,广州,510006
- 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
  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
- 基金信息：
  
  国家自然科学基金青年基金资助项目(62003092)
- DOI：10.12052/gdutxb.220074
  中图分类号： TG156
- 纸质出版：2022
- 稿件说明：
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彭积广, 肖涵臻. 模型预测控制下多移动机器人的跟踪与避障[J]. 广东工业大学学报, 2022, 39(5): 93-101.

Peng Ji-guang, Xiao Han-zhen. Tracking and Obstacle Avoidance of Multi-mobile Robots Under Model Predictive Control[J]. 2022, 39(5): 93-101.
彭积广, 肖涵臻. 模型预测控制下多移动机器人的跟踪与避障[J]. 广东工业大学学报, 2022, 39(5): 93-101. DOI： 10.12052/gdutxb.220074.

Peng Ji-guang, Xiao Han-zhen. Tracking and Obstacle Avoidance of Multi-mobile Robots Under Model Predictive Control[J]. 2022, 39(5): 93-101. DOI： 10.12052/gdutxb.220074.

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。

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.

关键词

Keywords

references

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