Trajectory tracking algorithm for mobile robots based on geometric model predictive control

doi:10.11772/j.issn.1001-9081.2024091273

Journal of Computer Applications

Trajectory tracking algorithm for mobile robots based on geometric model predictive control

GU Songjian^1,2, WU Fuxiang², GAO Xiangyang², YANG Mengjie³, ZHAN Yibing⁴, CHENG Jun²

1. Key Laboratory of Advanced Manufacturing and Automation Technology (Guilin University of Technology) 2. Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences 3. Shengyun Technology Company Limited 4. JD Explore Academy

Received:2024-09-06 Revised:2024-11-26 Online:2025-01-15 Published:2025-01-15
Contact: jun Cheng
About author:GU Songjian, born in 1998, M.S. candidate. His research interests include mobile robot control, embedded systems. WU Fuxiang, born in 1984, Ph.D., associate professor. His research interests include multimodal deep learning, image generation. GAO Xiangyang, born in 1982, M.S., senior engineer. His research interests include intelligent robots, embedded systems. YANG Mengjie, born in 1982, B.D., vice-senior. His research interests include artificial intelligence, smart cities, intelligent parks. ZHAN Yibing, born in 1990, Ph.D., algorithm scientist. His research interests include multi-modal learning; graph neural networks; large models. CHENG Jun, born in 1977, Ph.D., professor. His research interests include machine vision, intelligent robots, artificial intelligence.
Supported by:
National Key Innovation 2030—"New Generation Artificial Intelligence" Major Projects (2021ZD0111700); National Natural Science Foundation of China under Grant (U21A20487); Yunnan Science & Technology Project (202305AF150152).

基于几何模型预测控制的移动机器人轨迹跟踪算法

古松健¹,²,吴福祥²,高向阳²,杨梦杰³,詹忆冰⁴,程俊²

1.广西高校先进制造与自动化技术重点实验室（桂林理工大学） 2.中国科学院深圳先进技术研究院 3. 盛云科技有限公司 4. 京东探索研究院

通讯作者: 程俊
作者简介:古松健(1998—)，男，广东茂名人，硕士研究生，主要研究方向：移动机器人控制、嵌入式系统；吴福祥(1984—)，男，广东梅州人，副教授，博士，CCF会员，主要研究方向：多模态深度学习、图像生成；高向阳(1982—)，男，陕西咸阳人，高级工程师，硕士，主要研究方向：智能机器人、嵌入式系统；杨梦杰(1982—)，男，云南蒙自人，高级工程师，本科，主要研究方向：人工智能、智慧城市、智慧园区；詹忆冰(1990—)，男，湖北荆门人，副研究员，博士，主要研究方向：多模态学习、图神经网络、大模型；程俊(1977—)，男，安徽桐城人，教授，博士，主要研究方向：机器视觉、智能机器人、人工智能。
基金资助:
国家科技创新2030-“新一代人工智能”重大项目(2021ZD0111700);国家自然科学基金资助项目(U21A20487);云南省科技人才与平台计划(院士专家工作站)(202305AF150152)

Abstract

Abstract: To address the pose deviation issues faced by Wheeled Mobile Robot (WMR) during trajectory tracking due to inaccurate positioning and unknown disturbances, an Enhanced Particle Swarm Optimization Mixer (EPSO-Mixer) algorithm based on Geometric Model Predictive Control (GMPC) was proposed, aiming to enhance the trajectory tracking performance of WMRs. Firstly, an Enhanced Particle Swarm Optimization (EPSO) algorithm was proposed, which is based on Particle Swarm Optimization and can accelerate convergence speed and improve optimization capabilities. Secondly, EPSO is used to improve GMPC by selecting optimal tracking parameters according to the current deviation level to effectively reduce trajectory tracking errors. Thirdly, by integrating the Multi-Layer Perceptron Mixer (MLP-Mixer) architecture, the EPSO-Mixer algorithm was proposed, further enhancing the search capability for the global optimum and generating more adaptive control strategies. Simulation results show that, compared with nonlinear model predictive control and classic GMPC algorithms, EPSO-Mixer GMPC effectively improves the trajectory tracking performance of WMR under pose deviation conditions, reducing errors by approximately 16.4% to 82.3% and significantly mitigating vibration issues during motion. These results indicate that EPSO-Mixer provides more effective control strategies, not only reducing the complexity and time cost of parameter adjustment but also significantly enhancing the adaptability of trajectory tracking control.

Key words: Wheeled Mobile Robot (WMR), trajectory tracking, Particle Swarm Optimization (PSO), multi-layer perceptron mixer, Geometric Model Predictive Control (GMPC)

摘要： 摘要: 针对轮式移动机器人 (WMR) 在轨迹跟踪过程中因定位失准和未知干扰等因素导致的位姿偏移问题，提出了一种基于几何模型预测控制 (GMPC) 的增强型粒子群混合器 (EPSO-Mixer) 算法，旨在提升WMR的轨迹跟踪性能。首先，以粒子群优化为基础，提出了一种增强型粒子群优化(EPSO)算法，可以加快收敛速度并提升优化能力。其次，利用EPSO对GMPC进行改进，根据当前偏移程度筛选出最优跟踪参数以有效地减少轨迹跟踪误差。再次，结合混合全多层感知器 (MLP-Mixer)架构，提出了增强型粒子群混合器 (EPSO-Mixer) 算法，进一步增强对全局最优解的搜索能力，同时生成更具适应性的控制策略。仿真实验结果表明，与非线性模型预测控制和经典GMPC算法相比，EPSO-Mixer GMPC有效提升了WMR 在位姿偏移条件下的轨迹跟踪性能，误差减少约16.4%~82.3% ，并显著改善了运动中的振动问题。由此可见，EPSO-Mixer能够提供更有效的控制策略，不仅降低了参数调整的难度与时间成本，而且显著增强了轨迹跟踪控制的自适应能力。

关键词: 轮式移动机器人, 轨迹跟踪, 粒子群优化, 混合全多层感知器, 几何模型预测控制

CLC Number:

TP242.6

GU Songjian, WU Fuxiang, GAO Xiangyang, YANG Mengjie, ZHAN Yibing, CHENG Jun. Trajectory tracking algorithm for mobile robots based on geometric model predictive control[J]. Journal of Computer Applications, DOI: 10.11772/j.issn.1001-9081.2024091273.

古松健吴福祥高向阳杨梦杰詹忆冰程俊. 基于几何模型预测控制的移动机器人轨迹跟踪算法[J]. 《计算机应用》唯一官方网站, DOI: 10.11772/j.issn.1001-9081.2024091273.

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Trajectory tracking algorithm for mobile robots based on geometric model predictive control

基于几何模型预测控制的移动机器人轨迹跟踪算法

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