Advances in Parallel Robots and Mechanisms

A special issue of Machines (ISSN 2075-1702). This special issue belongs to the section "Robotics, Mechatronics and Intelligent Machines".

Deadline for manuscript submissions: 31 December 2024 | Viewed by 2815

Special Issue Editors


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Guest Editor
Department of Mechanical Engineering, University of Michigan, Ann Arbor, MI 48109, USA
Interests: parallel robots; cable-driven parallel robots; medical devices; smart manufacturing
Department of Mechanical Engineering, Tsinghua University, Beijing, China
Interests: parallel robotics and deployable mechanisms
Chow Yuk Ho Technology Centre for Innovative Medicine, The Chinese University of Hong Kong, Hong Kong, China
Interests: industrial robotics; cable-driven robotics; medical robotics; neural networks

Special Issue Information

Dear Colleagues,

As a complementary configuration of serial robots, parallel robots use multi-branched chains to jointly drive the end effector, which has the characteristics of high rigidity, high load capacity, quick dynamic response, and compact structure. The novel structure and characteristics of parallel robots have attracted extensive attention from both academia and industry. Innovative achievements have been made in mechanism design, optimization, control, etc., and have been successfully applied to high-speed pick-and-place, machine tools, simulators, medical devices, and so on.

The unique structure and excellent performance of parallel robots have driven researchers and developers in the community to explore continuously. In recent years, new types of parallel robots, such as cable-driven parallel robots, continuum robots, soft robots, underactuated robots, and hybrid robots, keep emerging. The application fields of parallel robots keep expanding, such as human–machine interaction, space capture, bionics, and medical and rehabilitation. New actuation methods, configurations, and application areas raise more scientific questions and chances.

This Special Issue will serve as a sharing platform for researchers and engineers to share and discuss the latest research results and explore the future developments of parallel robots. Papers are welcome on topics related to different aspects of structure design, modeling, simulation, optimization, control, and applications of parallel robots, including, but not limited to:

  • Synthesis and innovation design of parallel/hybrid robots;
  • Cable-driven, rigid-flexible, bionic, soft parallel robots;
  • Redundancy/reconfiguration of parallel/hybrid mechanisms;
  • Performance analysis, evaluation and optimization;
  • Precision assurance of high-performance parallel/hybrid mechanisms;
  • Advanced/intelligent control methods of parallel robots;
  • Novel concepts, state-of-the-art approaches/technologies in relevant fields;
  • Innovative applications of parallel robots and mechanisms, including human–machine interaction, medical/bionic devices, robotic machining, space capture, etc.

Dr. Zhaokun Zhang
Dr. Qizhi Meng
Dr. Zhiwei Cui
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Machines is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Published Papers (3 papers)

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Research

20 pages, 5833 KiB  
Article
Utilizing Reinforcement Learning to Drive Redundant Constrained Cable-Driven Robots with Unknown Parameters
by Dianjin Zhang and Bin Guo
Machines 2024, 12(6), 372; https://doi.org/10.3390/machines12060372 - 27 May 2024
Viewed by 172
Abstract
Cable-driven parallel robots (CDPRs) offer significant advantages, such as the lightweight design, large workspace, and easy reconfiguration, making them essential for various spatial applications and extreme environments. However, despite their benefits, CDPRs face challenges, notably the uncertainty in terms of the post-reconstruction parameters, [...] Read more.
Cable-driven parallel robots (CDPRs) offer significant advantages, such as the lightweight design, large workspace, and easy reconfiguration, making them essential for various spatial applications and extreme environments. However, despite their benefits, CDPRs face challenges, notably the uncertainty in terms of the post-reconstruction parameters, complicating cable coordination and impeding mechanism parameter identification. This is especially notable in CDPRs with redundant constraints, leading to cable relaxation or breakage. To tackle this challenge, this paper introduces a novel approach using reinforcement learning to drive redundant constrained cable-driven robots with uncertain parameters. Kinematic and dynamic models are established and applied in simulations and practical experiments, creating a conducive training environment for reinforcement learning. With trained agents, the mechanism is driven across 100 randomly selected parameters, resulting in a distinct directional distribution of the trajectories. Notably, the rope tension corresponding to 98% of the trajectory points is within the specified tension range. Experiments are carried out on a physical cable-driven device utilizing trained intelligent agents. The results indicate that the rope tension remained within the specified range throughout the driving process, with the end platform successfully maneuvered in close proximity to the designated target point. The consistency between the simulation and experimental results validates the efficacy of reinforcement learning in driving unknown parameters in redundant constraint-driven robots. Furthermore, the method’s applicability extends to mechanisms with diverse configurations of redundant constraints, broadening its scope. Therefore, reinforcement learning emerges as a potent tool for acquiring motion data in cable-driven mechanisms with unknown parameters and redundant constraints, effectively aiding in the reconstruction process of such mechanisms. Full article
(This article belongs to the Special Issue Advances in Parallel Robots and Mechanisms)
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22 pages, 403 KiB  
Article
Using Lie Derivatives with Dual Quaternions for Parallel Robots
by Stephen Montgomery-Smith and Cecil Shy
Machines 2023, 11(12), 1056; https://doi.org/10.3390/machines11121056 - 28 Nov 2023
Viewed by 877
Abstract
We introduce the notion of the Lie derivative in the context of dual quaternions that represent rigid motions and twists. First we define the wrench in terms of dual quaternions. Then we show how the Lie derivative helps understand how actuators affect an [...] Read more.
We introduce the notion of the Lie derivative in the context of dual quaternions that represent rigid motions and twists. First we define the wrench in terms of dual quaternions. Then we show how the Lie derivative helps understand how actuators affect an end effector in parallel robots, and make it explicit in the two cases case of Stewart Platforms, and cable-driven parallel robots. We also show how to use Lie derivatives with the Newton-Raphson Method to solve the forward kinematic problem for over constrained parallel actuators. Finally, we derive the equations of motion of the end effector in dual quaternion form, which include the effect of inertia from the actuators. Full article
(This article belongs to the Special Issue Advances in Parallel Robots and Mechanisms)
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19 pages, 5883 KiB  
Article
Dynamic Modeling and Performance Evaluation of a 5-DOF Hybrid Robot for Composite Material Machining
by Xiaojian Wang, Jun Wu and Yulin Zhou
Machines 2023, 11(6), 652; https://doi.org/10.3390/machines11060652 - 16 Jun 2023
Viewed by 1005
Abstract
Dynamic performance is an important performance of robots used for machine processing. This paper studies the dynamic modeling and evaluation method of a 5-DOF (Degree of Freedom) hybrid robot used in aerospace composite material processing. With the consideration of the dynamics of the [...] Read more.
Dynamic performance is an important performance of robots used for machine processing. This paper studies the dynamic modeling and evaluation method of a 5-DOF (Degree of Freedom) hybrid robot used in aerospace composite material processing. With the consideration of the dynamics of the serial part, the complete dynamic model of the hybrid robot is established based on the virtual work principle. In addition to the widely considered acceleration term, a dynamic performance evaluation index that comprehensively considers the acceleration term, velocity term and gravity term in the dynamic model is proposed. Using the dynamic performance index, the effect of the placement direction of the robot and the arrangement of the double symmetric limbs on robot dynamics are investigated. The results indicate that the vertical placement is beneficial to the dynamics of the hybrid robot, and the arrangement of double symmetric limbs has different effects on different limbs. Full article
(This article belongs to the Special Issue Advances in Parallel Robots and Mechanisms)
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