Data-Driven Dynamics Modeling and Control Strategy for a Planar n-DOF Cable-Driven Parallel Robot Driven by n+1 Cables Allowing Collisions

IF 2.2 4区 计算机科学 Q2 ENGINEERING, MECHANICAL Journal of Mechanisms and Robotics-Transactions of the Asme Pub Date : 2023-06-21 DOI:10.1115/1.4062792
Genyuan Xu, Haoda Zhu, Hao Xiong, Y. Lou
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引用次数: 1

Abstract

Researchers have proposed to allow collisions of cables with the base, the end-effector, or obstacles to expand the workspace of Cable-Driven Parallel Robots (CDPRs) in recent years. However, allowing collisions also leads to new challenges in kinematics and dynamics modeling for CDPRs. To this end, this paper focuses on a planar fully-constrained n-Degree-of-Freedom (DOF) CDPR driven by n + 1 cables allowing collisions and develops a data-driven dynamics modeling strategy. The data-driven dynamics modeling strategy can address the collisions and optimal tension distribution issues simultaneously. Based on the data-driven dynamics modeling strategy, this paper proposes a data-driven dynamics-based control strategy for the planar CDPR allowing collisions. A planar two-DOF CDPR prototype driven by three cables is established to evaluate the data-driven dynamics modeling strategy and data-driven dynamics-based control strategy.
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n+1缆索碰撞驱动平面n自由度缆索驱动并联机器人的数据驱动动力学建模与控制策略
近年来,研究人员提出允许电缆与底座、末端执行器或障碍物碰撞,以扩大电缆驱动并联机器人(CDPR)的工作空间。然而,允许碰撞也会给CDPR的运动学和动力学建模带来新的挑战。为此,本文重点研究了由n+1根允许碰撞的电缆驱动的平面全约束n自由度CDPR,并开发了一种数据驱动的动力学建模策略。数据驱动的动力学建模策略可以同时解决碰撞和最佳张力分布问题。在数据驱动动力学建模策略的基础上,本文提出了一种允许碰撞的平面CDPR基于数据驱动动力学的控制策略。建立了一个由三根拉索驱动的平面二自由度CDPR原型,以评估数据驱动动力学建模策略和基于数据驱动动力学的控制策略。
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来源期刊
CiteScore
5.60
自引率
15.40%
发文量
131
审稿时长
4.5 months
期刊介绍: Fundamental theory, algorithms, design, manufacture, and experimental validation for mechanisms and robots; Theoretical and applied kinematics; Mechanism synthesis and design; Analysis and design of robot manipulators, hands and legs, soft robotics, compliant mechanisms, origami and folded robots, printed robots, and haptic devices; Novel fabrication; Actuation and control techniques for mechanisms and robotics; Bio-inspired approaches to mechanism and robot design; Mechanics and design of micro- and nano-scale devices.
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