A Distributed Model Predictive Control for Multiple Mobile Robots with the Model Uncertainty

Abstract

In this paper, a distributed predictive control with the model uncertainty which uses the data-driven strategy and robust theory (data-driven RDMPC) is proposed for the formation control of multiple mobile robots. The robust performance objective minimization is applied to replace the quadratic performance objective minimization to establish the optimization problem, where the model uncertainty is considered in the distributed system. The control policy is derived by applying the data-driven strategy, and the future predictive value is obtained by employing the linear law in the historical data. Lyapunov theory is referred to analyze the stability of the mobile robot formation system. The effectiveness of the proposed method is proved by a set of simulation experiments.

Navigation Strategy for Mobile Robot Based on Computer Vision and Yolov5 Network in the Unknown Environment

“…The development of intelligent robots capable of autonomous navigation in unknown environments is the primary focus of mobile robot navigation. It is a multidisciplinary field that combines techniques and technologies from robotics (Hichri et al, 2021;Nguyen & Vu, 2022;Thuan et al, 2023), computer vision (Marroquín et al, 2023), machine learning (Geng et al, 2018;Xiao et al, 2022), and control theory (Ren et al, 2022;Shang et al, 2021). The ultimate objective of mobile robot navigation research is to create automated systems that can operate safely and efficiently in real-world environments, performing tasks such as exploration, surveillance, delivery, and transportation.…”