Design and Validation of New Acceleration-Level Repetitive Motion Planning Scheme for Omnidirectional Mobile Robotic Manipulators

IF 8.9 1区计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

IEEE Internet of Things Journal Pub Date : 2025-03-29 DOI:10.1109/JIOT.2025.3574846

Naimeng Cang;Dongsheng Guo;Feng Qiu;Xianjun Chen;Weidong Zhang

引用次数: 0

Abstract

Achieving repetitive motion planning (RMP) is essential in the study of mobile robot manipulators. This article presents an acceleration-level RMP (ALRMP) scheme for omnidirectional mobile robotic manipulators (OMRMs). Specifically, a new acceleration-level performance index is designed to realize RMP using the gradient-dynamics and neurodynamics methods. Leveraging this index and incorporating physical constraints (i.e., position-level, velocity-level, and acceleration-level limits), a novel ALRMP scheme is proposed and analyzed. The scheme is formulated as a quadratic program (QP) and solved using a neural network solver. Comparative simulations conducted on an OMRM demonstrate the effectiveness and superiority of the proposed ALRMP scheme over the velocity-level RMP (VLRMP) scheme. The applicable potential of the proposed ALRMP scheme is further indicated via the real-world experiment on a practical OMRM system.

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全向移动机械臂加速度级重复运动规划新方案的设计与验证

实现重复运动规划（RMP）是移动机器人机械臂研究的关键。提出了一种面向全向移动机器人的加速度级RMP （ALRMP）方案。具体而言，利用梯度动力学和神经动力学方法设计了一种新的加速度级性能指标来实现RMP。利用该指标并结合物理约束（即位置级、速度级和加速度级限制），提出并分析了一种新的ALRMP方案。该方案被表述为一个二次规划（QP），并使用神经网络求解器进行求解。通过在OMRM上的仿真对比，验证了ALRMP方案相对于速度级RMP方案的有效性和优越性。通过在实际的OMRM系统上的实际实验，进一步表明了所提出的ALRMP方案的应用潜力。

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

IEEE Internet of Things Journal Computer Science-Information Systems

CiteScore

17.60

自引率

13.20%

发文量

1982

期刊介绍： The EEE Internet of Things (IoT) Journal publishes articles and review articles covering various aspects of IoT, including IoT system architecture, IoT enabling technologies, IoT communication and networking protocols such as network coding, and IoT services and applications. Topics encompass IoT's impacts on sensor technologies, big data management, and future internet design for applications like smart cities and smart homes. Fields of interest include IoT architecture such as things-centric, data-centric, service-oriented IoT architecture; IoT enabling technologies and systematic integration such as sensor technologies, big sensor data management, and future Internet design for IoT; IoT services, applications, and test-beds such as IoT service middleware, IoT application programming interface (API), IoT application design, and IoT trials/experiments; IoT standardization activities and technology development in different standard development organizations (SDO) such as IEEE, IETF, ITU, 3GPP, ETSI, etc.