Towards Cooperative Transportation of Multiple Mecanum-Wheeled Automated Guided Vehicles

IF 1 Q4 AUTOMATION & CONTROL SYSTEMS

Mechatronic Systems and Control Pub Date : 2019-11-26 DOI:10.1115/dscc2019-9141

J. Peng, Yen‐Chen Liu

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引用次数: 3

Abstract

Utilizing multiple small-sized automated guided vehicles (AGVs) in cooperatively transport large and heavy objects in manufacturing factories or logistics is an emerging research direction. Flexibility and efficiency can be enhanced by using multi-AGV comparing to a large AGV with higher capacity especially in clutter environments. In this paper, a multi-AGV system by using Mecanum wheels to provide omnidirectional movement is proposed for cooperative transportation. Accordingly, the proposed Mecanum-wheeled automated guided vehicles (MWAGVs) composed of Mecanum wheels and a rotary platform provides not only non-constrained movement but also planar displacement for allowance of distance errors. In the proposed MWAGVs, the formation control with fixed geometry during operation is significant especially with unknown object information, dynamic uncertainties, and external disturbances. Therefore, the passivity-based adaptive synchronizing control algorithm is developed to ensure stability and tracking performance with uncertain dynamic parameters. Simulations and Experiments show the efficacy of designed Mecanum-wheeled AGV.

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多机轮自动引导车辆协同运输研究

利用多辆小型自动导引车(agv)协同运输制造工厂或物流中的大型和重型物品是一个新兴的研究方向。与容量较大的大型AGV相比，使用多AGV可以提高系统的灵活性和效率，特别是在杂波环境下。本文提出了一种利用机械轮提供全方位运动的多agv协同运输系统。因此，所提出的由Mecanum轮和旋转平台组成的Mecanum轮式自动导向车(mwagv)不仅可以提供无约束的运动，还可以提供平面位移以考虑距离误差。在mwagv系统中，特别是在存在未知目标信息、动态不确定性和外部干扰的情况下，具有固定几何形状的编队控制具有重要意义。为此，为了保证系统在动态参数不确定情况下的稳定性和跟踪性能，提出了基于无源性的自适应同步控制算法。仿真和实验验证了所设计的机械轮式AGV的有效性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Mechatronic Systems and Control AUTOMATION & CONTROL SYSTEMS-

CiteScore

1.40

自引率

66.70%

发文量

期刊介绍： This international journal publishes both theoretical and application-oriented papers on various aspects of mechatronic systems, modelling, design, conventional and intelligent control, and intelligent systems. Application areas of mechatronics may include robotics, transportation, energy systems, manufacturing, sensors, actuators, and automation. Techniques of artificial intelligence may include soft computing (fuzzy logic, neural networks, genetic algorithms/evolutionary computing, probabilistic methods, etc.). Techniques may cover frequency and time domains, linear and nonlinear systems, and deterministic and stochastic processes. Hybrid techniques of mechatronics that combine conventional and intelligent methods are also included. First published in 1972, this journal originated with an emphasis on conventional control systems and computer-based applications. Subsequently, with rapid advances in the field and in view of the widespread interest and application of soft computing in control systems, this latter aspect was integrated into the journal. Now the area of mechatronics is included as the main focus. A unique feature of the journal is its pioneering role in bridging the gap between conventional systems and intelligent systems, with an equal emphasis on theory and practical applications, including system modelling, design and instrumentation. It appears four times per year.