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Biomimetics
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Human-Inspired Grasp Control in Robotics
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Human-Inspired Grasp Control in Robotics

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

Deadline for manuscript submissions: closed (15 October 2024) | Viewed by 3289

Special Issue Editors

Dr. Yi Zhang

E-Mail Website
Guest Editor
School of Mechanical and Electrical Engineering, Central South University, Changsha 410083, China
Interests: mechanism design; robotic system and technology; biomechatronics; complex system modeling and control
Prof. Dr. Junzhi Yu

E-Mail Website
Guest Editor
Department of Advanced Manufacturing and Robotics, College of Engineering, Peking University, Beijing 100871, China
Interests: intelligent robots; advanced robot control; embedded vision
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Humans have a remarkable ability to grasp and manipulate various objects. This Special Issue, entitled "Human-Inspired Grasp Control in Robotics," tackles the challenge of enabling robots to efficiently and intuitively manipulate objects in real-world scenarios. The objective is to replicate the way humans handle objects in order to enhance the adaptability and dexterity of robotic systems. This research holds practical implications for fields such as prosthetics, manufacturing, healthcare, and household robotics, where precise and flexible object manipulation is crucial.

Traditional approaches to grasping control in robotics rely on rigid grasping models that struggle to cope with the dynamic nature of real-world environments. To address this drawback, researchers propose a human-inspired approach that draws inspiration from how humans utilize tactile sensing and feedback to adjust their grip on objects. This approach comprises key elements such as tactile sensing, object recognition, adaptive planning algorithms, and so on. It can empower the robot to adapt its grip based on the unique characteristics of an object and environmental constraints, ultimately enhancing grasp stability and success rates. It underscores the significance of emulating human grasping strategies in order to elevate the adaptability and dexterity of robotic systems. The proposed techniques hold immense potential for advancing robotic manipulation capabilities across a wide array of practical real-world tasks.

Dr. Yi Zhang
Prof. Dr. Junzhi Yu
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. 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

  • human-inspired grasping
  • reinforcement learning
  • tactile sensing
  • object recognition
  • force control
  • position control
  • robotic grasping
  • robot–environment interaction
  • dexterous grasping
  • grasp posture

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Published Papers (4 papers)

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Research

23 pages, 10315 KiB  
Article
The Design and Adaptive Control of a Parallel Chambered Pneumatic Muscle-Driven Soft Hand Robot for Grasping Rehabilitation
by Zhixiong Zhou, Qingsong Ai, Mengnan Li, Wei Meng, Quan Liu and Sheng Quan Xie
Biomimetics 2024, 9(11), 706; https://doi.org/10.3390/biomimetics9110706 - 18 Nov 2024
Viewed by 375
Abstract
The widespread application of exoskeletons driven by soft actuators in motion assistance and medical rehabilitation has proven effective for patients who struggle with precise object grasping and suffer from insufficient hand strength due to strokes or other conditions. Repetitive passive flexion/extension exercises and [...] Read more.
The widespread application of exoskeletons driven by soft actuators in motion assistance and medical rehabilitation has proven effective for patients who struggle with precise object grasping and suffer from insufficient hand strength due to strokes or other conditions. Repetitive passive flexion/extension exercises and active grasp training are known to aid in the restoration of motor nerve function. However, conventional pneumatic artificial muscles (PAMs) used for hand rehabilitation typically allow for bending in only one direction, thereby limiting multi-degree-of-freedom movements. Moreover, establishing precise models for PAMs is challenging, making accurate control difficult to achieve. To address these challenges, we explored the design and fabrication of a bidirectionally bending PAM. The design parameters were optimized based on actual rehabilitation needs and a finite element analysis. Additionally, a dynamic model for the PAM was established using elastic strain energy and the Lagrange equation. Building on this, an adaptive position control method employing a radial basis function neural network, optimized for parameters and hidden layer nodes, was developed to enhance the accuracy of these soft PAMs in assisting patients with hand grasping. Finally, a wearable soft hand rehabilitation exoskeleton was designed, offering two modes, passive training and active grasp, aimed at helping patients regain their grasp ability. Full article
(This article belongs to the Special Issue Human-Inspired Grasp Control in Robotics)
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15 pages, 3701 KiB  
Article
Compliant Grasp Control Method for the Underactuated Prosthetic Hand Based on the Estimation of Grasping Force and Muscle Stiffness with sEMG
by Xiaolei Xu, Hua Deng, Yi Zhang and Nianen Yi
Biomimetics 2024, 9(11), 658; https://doi.org/10.3390/biomimetics9110658 - 27 Oct 2024
Viewed by 660
Abstract
Human muscles can generate force and stiffness during contraction. When in contact with objects, human hands can achieve compliant grasping by adjusting the grasping force and the muscle stiffness based on the object’s characteristics. To realize humanoid-compliant grasping, most prosthetic hands obtain the [...] Read more.
Human muscles can generate force and stiffness during contraction. When in contact with objects, human hands can achieve compliant grasping by adjusting the grasping force and the muscle stiffness based on the object’s characteristics. To realize humanoid-compliant grasping, most prosthetic hands obtain the stiffness parameter of the compliant controller according to the environmental stiffness, which may be inconsistent with the amputee’s intention. To address this issue, this paper proposes a compliant grasp control method for an underactuated prosthetic hand that can directly obtain the control signals for compliant grasping from surface electromyography (sEMG) signals. First, an estimation method of the grasping force is established based on the Huxley muscle model. Then, muscle stiffness is estimated based on the muscle contraction principle. Subsequently, a relationship between the muscle stiffness of the human hand and the stiffness parameters of the prosthetic hand controller is established based on fuzzy logic to realize compliant grasp control for the underactuated prosthetic hand. Experimental results indicate that the prosthetic hand can adjust the desired force and stiffness parameters of the impedance controller based on sEMG, achieving a quick and stable grasp as well as a slow and gentle grasp on different objects. Full article
(This article belongs to the Special Issue Human-Inspired Grasp Control in Robotics)
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18 pages, 6112 KiB  
Article
A Globally Guided Dual-Arm Reactive Motion Controller for Coordinated Self-Handover in a Confined Domestic Environment
by Zihang Geng, Zhiyuan Yang, Wei Xu, Weichao Guo and Xinjun Sheng
Biomimetics 2024, 9(10), 629; https://doi.org/10.3390/biomimetics9100629 - 16 Oct 2024
Viewed by 666
Abstract
Future humanoid robots will be widely deployed in our daily lives. Motion planning and control in an unstructured, confined, and human-centered environment utilizing dexterity and a cooperative ability of dual-arm robots is still an open issue. We propose a globally guided dual-arm reactive [...] Read more.
Future humanoid robots will be widely deployed in our daily lives. Motion planning and control in an unstructured, confined, and human-centered environment utilizing dexterity and a cooperative ability of dual-arm robots is still an open issue. We propose a globally guided dual-arm reactive motion controller (GGDRC) that combines the strengths of global planning and reactive methods. In this framework, a global planner module with a prospective task horizon provides feasible guidance in a Cartesian space, and a local reactive controller module addresses real-time collision avoidance and coordinated task constraints through the exploitation of dual-arm redundancy. GGDRC extends the start-of-the-art optimization-based reactive method for motion-restricted dynamic scenarios requiring dual-arm cooperation. We design a pick–handover–place task to compare the performances of these two methods. Results demonstrate that GGDRC exhibits accurate collision avoidance precision (5 mm) and a high success rate (84.5%). Full article
(This article belongs to the Special Issue Human-Inspired Grasp Control in Robotics)
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13 pages, 1007 KiB  
Article
A Fast Grasp Planning Algorithm for Humanoid Robot Hands
by Ziqi Liu, Li Jiang and Ming Cheng
Biomimetics 2024, 9(10), 599; https://doi.org/10.3390/biomimetics9100599 - 4 Oct 2024
Viewed by 737
Abstract
Grasp planning is crucial for robots to perform precision grasping tasks, where determining the grasp points significantly impacts the performance of the robotic hand. Currently, the majority of grasp planning methods based on analytic approaches solve the problem by transforming it into a [...] Read more.
Grasp planning is crucial for robots to perform precision grasping tasks, where determining the grasp points significantly impacts the performance of the robotic hand. Currently, the majority of grasp planning methods based on analytic approaches solve the problem by transforming it into a nonlinear constrained planning problem. This method often requires performing convex hull computations, which tend to have high computational complexity. This paper proposes a new algorithm for calculating multi-finger force-closure grasps of three-dimensional objects based on humanoid multi-fingered hands. Firstly, sufficient conditions for the multi-finger force-closure grasps of three-dimensional objects are derived from a point contact model with friction. These three-dimensional force-closure conditions are then transformed into two-dimensional plane conditions, leading to a simple algorithm for multi-finger force-closure determination. This method is purely based on geometric analysis, resulting in low computational demands and enabling the rapid assessment of force-closure grasps, which are beneficial for real-time applications. Finally, the algorithm is validated through two case studies, demonstrating its feasibility and effectiveness. Full article
(This article belongs to the Special Issue Human-Inspired Grasp Control in Robotics)
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