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Biomimetics
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Bionic Robot Hand: Dexterous Manipulation and Robust Grasping
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Bionic Robot Hand: Dexterous Manipulation and Robust Grasping

A special issue of Biomimetics (ISSN 2313-7673). This special issue belongs to the section "Locomotion and Bioinspired Robotics".

Deadline for manuscript submissions: closed (30 April 2023) | Viewed by 8641

Special Issue Editor

Dr. Dapeng Yang

E-Mail Website
Guest Editor
State Key Laboratory of Robotics and System, Harbin Institute of Technology, Harbin 150080, China
Interests: dexterous prosthesis; bionic robot hand; manipulation and grasping; artificial intelligence

Special Issue Information

Dear Colleagues,

Hands are an important medium for humans to interact with the natural world. In the field of robotics, researchers are devoted to replicating the powerful operation and perception capabilities of human hands through biomimetic technology using various engineering approaches and have produced a wide range of exciting prototypes or products in the fields of aerospace, industrial production, medical rehabilitation, and others. However, the dexterity, adaptability, reliability, and intelligence of current robotic hands are still far from their biological template. Determining how to endow robotic hands with smart biological traits, increasing grasping functionality but decreasing operational complexity, is a core problem to be solved in the future development of biomimetic robot hands.

This Special Issue on “Bionic Robot Hand: Dexterous Manipulation and Robust Grasping” calls for original contributions on practical problems in the design as well as use of bionic robot hands. The contributions should focus on how to improve the functionality and intelligence of bionic hands and other hand-related issues, showing unique mechatronic design, exact theoretical derivation, and adequate experimental results. In this Special Issue, also of special interest are biologically inspired designs (such as soft material and origami structure) together with their potential applications, as these novel designs may have better interactivity in more and more human–machine coexistence scenarios in the future.

Dr. Dapeng Yang
Guest Editor

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. Biomimetics 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 2200 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.

Keywords

  • robot hand
  • bio-inspired
  • manipulation
  • grasping
  • tactile/haptic
  • soft
  • origami
  • prosthesis
  • end-effector

Published Papers (5 papers)

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Research

15 pages, 5519 KiB  
Article
Design of a Multi-Mode Mechanical Finger Based on Linkage and Tendon Fusion Transmission
by Yi Zhang, Qian Zhao, Hua Deng and Xiaolei Xu
Biomimetics 2023, 8(3), 316; https://doi.org/10.3390/biomimetics8030316 - 17 Jul 2023
Viewed by 1164
Abstract
Today, most humanoid mechanical fingers use an underactuated mechanism driven by linkages or tendons, with only a single and fixed grasping trajectory. This paper proposes a new multi-mode humanoid finger mechanism based on linkage and tendon fusion transmission, which is embedded with an [...] Read more.
Today, most humanoid mechanical fingers use an underactuated mechanism driven by linkages or tendons, with only a single and fixed grasping trajectory. This paper proposes a new multi-mode humanoid finger mechanism based on linkage and tendon fusion transmission, which is embedded with an adjustable-length tendon mechanism to achieve three types of grasping mode. The structural parameters of the mechanism are optimized according to the kinematic and static models. Furthermore, a discussion was conducted on how to set the speed ratio of the linkage driving motor and the tendon driving motor to adjust the length and tension of the tendon, in order to achieve the switching of the shape-adaptive, coupled-adaptive, and variable coupling-adaptive grasping modes. Finally, the multi-mode functionality of the proposed finger mechanism was verified through multiple grasping experiments. Full article
(This article belongs to the Special Issue Bionic Robot Hand: Dexterous Manipulation and Robust Grasping)
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19 pages, 8641 KiB  
Article
A High-Efficient Reinforcement Learning Approach for Dexterous Manipulation
by Jianhua Zhang, Xuanyi Zhou, Jinyu Zhou, Shiming Qiu, Guoyuan Liang, Shibo Cai and Guanjun Bao
Biomimetics 2023, 8(2), 264; https://doi.org/10.3390/biomimetics8020264 - 16 Jun 2023
Viewed by 1412
Abstract
Robotic hands have the potential to perform complex tasks in unstructured environments owing to their bionic design, inspired by the most agile biological hand. However, the modeling, planning and control of dexterous hands remain unresolved, open challenges, resulting in the simple movements and [...] Read more.
Robotic hands have the potential to perform complex tasks in unstructured environments owing to their bionic design, inspired by the most agile biological hand. However, the modeling, planning and control of dexterous hands remain unresolved, open challenges, resulting in the simple movements and relatively clumsy motions of current robotic end effectors. This paper proposed a dynamic model based on generative adversarial architecture to learn the state mode of the dexterous hand, reducing the model’s prediction error in long spans. An adaptive trajectory planning kernel was also developed to generate High-Value Area Trajectory (HVAT) data according to the control task and dynamic model, with adaptive trajectory adjustment achieved by changing the Levenberg–Marquardt (LM) coefficient and the linear searching coefficient. Furthermore, an improved Soft Actor–Critic (SAC) algorithm is designed by combining maximum entropy value iteration and HVAT value iteration. An experimental platform and simulation program were built to verify the proposed method with two manipulating tasks. The experimental results indicate that the proposed dexterous hand reinforcement learning algorithm has better training efficiency and requires fewer training samples to achieve quite satisfactory learning and control performance. Full article
(This article belongs to the Special Issue Bionic Robot Hand: Dexterous Manipulation and Robust Grasping)
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14 pages, 6228 KiB  
Article
Hand Grasp Pose Prediction Based on Motion Prior Field
by Xu Shi, Weichao Guo, Wei Xu and Xinjun Sheng
Biomimetics 2023, 8(2), 250; https://doi.org/10.3390/biomimetics8020250 - 12 Jun 2023
Viewed by 1528
Abstract
Shared control of bionic robot hands has recently attracted much research attention. However, few studies have performed predictive analysis for grasp pose, which is vital for the pre-shape planning of robotic wrists and hands. Aiming at shared control of dexterous hand grasp planning, [...] Read more.
Shared control of bionic robot hands has recently attracted much research attention. However, few studies have performed predictive analysis for grasp pose, which is vital for the pre-shape planning of robotic wrists and hands. Aiming at shared control of dexterous hand grasp planning, this paper proposes a framework for grasp pose prediction based on the motion prior field. To map the hand–object pose to the final grasp pose, an object-centered motion prior field is established to learn the prediction model. The results of motion capture reconstruction show that, with the input of a 7-dimensional pose and cluster manifolds of dimension 100, the model performs best in terms of prediction accuracy (90.2%) and error distance (1.27 cm) in the sequence. The model makes correct predictions in the first 50% of the sequence during hand approach to the object. The outcomes of this study enable prediction of the grasp pose in advance as the hand approaches the object, which is very important for enabling the shared control of bionic and prosthetic hands. Full article
(This article belongs to the Special Issue Bionic Robot Hand: Dexterous Manipulation and Robust Grasping)
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11 pages, 1995 KiB  
Article
Finger Kinematics during Human Hand Grip and Release
by Xiaodong Li, Rongwei Wen, Dehao Duanmu, Wei Huang, Kinto Wan and Yong Hu
Biomimetics 2023, 8(2), 244; https://doi.org/10.3390/biomimetics8020244 - 08 Jun 2023
Cited by 3 | Viewed by 1770
Abstract
A bionic robotic hand can perform many movements similar to a human hand. However, there is still a significant gap in manipulation between robot and human hands. It is necessary to understand the finger kinematics and motion patterns of human hands to improve [...] Read more.
A bionic robotic hand can perform many movements similar to a human hand. However, there is still a significant gap in manipulation between robot and human hands. It is necessary to understand the finger kinematics and motion patterns of human hands to improve the performance of robotic hands. This study aimed to comprehensively investigate normal hand motion patterns by evaluating the kinematics of hand grip and release in healthy individuals. The data corresponding to rapid grip and release were collected from the dominant hands of 22 healthy people by sensory glove. The kinematics of 14 finger joints were analyzed, including the dynamic range of motion (ROM), peak velocity, joint sequence and finger sequence. The results show that the proximal interphalangeal (PIP) joint had a larger dynamic ROM than metacarpophalangeal (MCP) and distal interphalangeal (DIP) joints. Additionally, the PIP joint had the highest peak velocity, both in flexion and extension. For joint sequence, the PIP joint moved prior to the DIP or MCP joints during flexion, while extension started in DIP or MCP joints, followed by the PIP joint. Regarding the finger sequence, the thumb started to move before the four fingers, and stopped moving after the fingers during both grip and release. This study explored the normal motion patterns in hand grip and release, which provided a kinematic reference for the design of robotic hands and thus contributes to its development. Full article
(This article belongs to the Special Issue Bionic Robot Hand: Dexterous Manipulation and Robust Grasping)
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13 pages, 12401 KiB  
Article
A Highly Compact Zip Chain Arm with Origami-Inspired Folding Chain Structures
by Dong-Ki Kim and Gwang-Pil Jung
Biomimetics 2023, 8(2), 176; https://doi.org/10.3390/biomimetics8020176 - 24 Apr 2023
Viewed by 1878
Abstract
A deployable robotic arm can be a useful tool for mobile systems to widen accessible areas without removing mobility. For practical use, the deployable robotic arm needs to satisfy two requirements: a high extension–compression ratio and robust structural stiffness against the environment. To [...] Read more.
A deployable robotic arm can be a useful tool for mobile systems to widen accessible areas without removing mobility. For practical use, the deployable robotic arm needs to satisfy two requirements: a high extension–compression ratio and robust structural stiffness against the environment. To this end, this paper suggests, for the first time, an origami-inspired zipper chain to achieve a highly compact, one-degree-of-freedom zipper chain arm. The key component is the foldable chain, which innovatively increases the space-saving capability in the stowed state. The foldable chain is fully flattened in the stowed state, allowing for storage of many more chains in the same space. Moreover, a transmission system was designed to transform a 2D flat pattern into a 3D chain shape in order to control the length of the origami zipper. Additionally, an empirical parametric study was performed to choose design parameters to maximize the bending stiffness. For the feasibility test, a prototype was built and performance tests were executed in relation to extension length, speed, and structural robustness. Full article
(This article belongs to the Special Issue Bionic Robot Hand: Dexterous Manipulation and Robust Grasping)
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