Biomimetics

Research

17 pages, 3450 KiB

Open AccessArticle

Design and Optimization of an Anthropomorphic Robot Finger

by Ming Cheng, Li Jiang and Ziqi Liu

Biomimetics 2025, 10(3), 170; https://doi.org/10.3390/biomimetics10030170 - 11 Mar 2025

Viewed by 654

The coupled-adaptive underactuated finger offers two motion modes: pre-grasping and self-adaptive grasping. It can execute anthropomorphic pre-grasp motions before the proximal phalanx contacts an object and transitions to adaptive enveloping once contact occurs. The key to designing a coupled-adaptive finger lies in its [...] Read more.

The coupled-adaptive underactuated finger offers two motion modes: pre-grasping and self-adaptive grasping. It can execute anthropomorphic pre-grasp motions before the proximal phalanx contacts an object and transitions to adaptive enveloping once contact occurs. The key to designing a coupled-adaptive finger lies in its configuration and parameter, which are crucial for achieving a more human-like design for the prosthetic hand. Thus, this paper proposes a configuration topology and parameter optimization design method for a three-joint coupled-adaptive underactuated finger. The finger mechanism utilizes a combination of prismatic pairs and a compression spring to facilitate the transition between coupled motion and adaptive motion. This enables the underactuated finger to perform coupled movements in free space and adaptive grasping motions once it makes contact with an object. Furthermore, this paper introduces a finger linkage parameter optimization method that takes the joint motion angles and overall dimensions as constraints, aiming to linearize the joint coupling motion ratios as the primary optimization objective. The design method proposed in this paper not only presents a novel linkage mechanism but also outlines and compares its isomorphic types. Furthermore, the optimization results provide an accurate maximum motion value for the finger. Full article

(This article belongs to the Special Issue Human-Inspired Grasp Control in Robotics)

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20 pages, 3134 KiB

Open AccessArticle

Design of Novel Human Wrist Prostheses Based on Parallel Architectures: Dimensional Synthesis and Kinetostatics

by Raffaele Di Gregorio

Biomimetics 2025, 10(1), 44; https://doi.org/10.3390/biomimetics10010044 - 12 Jan 2025

Viewed by 932

Abstract

The human wrist affects the ability to efficiently perform many manipulation tasks. Despite this, most upper-limb prostheses are focused on the hand’s mobility, which makes users compensate for the lost wrist mobility with complex manipulation strategies relying on the mobility of other body [...] Read more.

The human wrist affects the ability to efficiently perform many manipulation tasks. Despite this, most upper-limb prostheses are focused on the hand’s mobility, which makes users compensate for the lost wrist mobility with complex manipulation strategies relying on the mobility of other body parts. In this context, research on wrist prostheses is still open to new contributions, even though a number of such prostheses are already present in the literature and on the market. In particular, the potential uses of parallel mechanisms in wrist prosthesis design have not been fully explored yet. In this work, after recalling the mobility characteristics of human wrists and reviewing the literature both on wrist prostheses and parallel mechanisms, a number of parallel architectures employable in a wrist prosthesis are selected. Then, with reference to the design requirements of this prosthesis type, the dimensional synthesis and kinetostatic analysis of the selected architectures are addressed. The results of this work are new wrist prosthesis architectures together with the analysis of their kinetostatic performances. These findings complete the first step of a research project aimed at developing new concepts for mechatronic wrists. Full article

(This article belongs to the Special Issue Human-Inspired Grasp Control in Robotics)

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19 pages, 3359 KiB

Open AccessArticle

MS-CLSTM: Myoelectric Manipulator Gesture Recognition Based on Multi-Scale Feature Fusion CNN-LSTM Network

by Ziyi Wang, Wenjing Huang, Zikang Qi and Shuolei Yin

Biomimetics 2024, 9(12), 784; https://doi.org/10.3390/biomimetics9120784 - 23 Dec 2024

Cited by 1 | Viewed by 1326

Abstract

Surface electromyography (sEMG) signals reflect the local electrical activity of muscle fibers and the synergistic action of the overall muscle group, making them useful for gesture control of myoelectric manipulators. In recent years, deep learning methods have increasingly been applied to sEMG gesture [...] Read more.

Surface electromyography (sEMG) signals reflect the local electrical activity of muscle fibers and the synergistic action of the overall muscle group, making them useful for gesture control of myoelectric manipulators. In recent years, deep learning methods have increasingly been applied to sEMG gesture recognition due to their powerful automatic feature extraction capabilities. sEMG signals contain rich local details and global patterns, but single-scale convolutional networks are limited in their ability to capture both comprehensively, which restricts model performance. This paper proposes a deep learning model based on multi-scale feature fusion—MS-CLSTM (MS Block-ResCBAM-Bi-LSTM). The MS Block extracts local details, global patterns, and inter-channel correlations in sEMG signals using convolutional kernels of different scales. The ResCBAM, which integrates CBAM and Simple-ResNet, enhances attention to key gesture information while alleviating overfitting issues common in small-sample datasets. Experimental results demonstrate that the MS-CLSTM model achieves recognition accuracies of 86.66% and 83.27% on the Ninapro DB2 and DB4 datasets, respectively, and the accuracy can reach 89% in real-time myoelectric manipulator gesture prediction experiments. The proposed model exhibits superior performance in sEMG gesture recognition tasks, offering an effective solution for applications in prosthetic hand control, robotic control, and other human–computer interaction fields. Full article

(This article belongs to the Special Issue Human-Inspired Grasp Control in Robotics)

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16 pages, 3208 KiB

Open AccessArticle

Biomimetic Strategies of Slip Sensing, Perception, and Protection in Prosthetic Hand Grasp

by Anran Xie, Zhuozhi Zhang, Jie Zhang, Tie Li, Weidong Chen, James Patton and Ning Lan

Biomimetics 2024, 9(12), 751; https://doi.org/10.3390/biomimetics9120751 - 11 Dec 2024

Viewed by 1250

Abstract

This study develops biomimetic strategies for slip prevention in prosthetic hand grasps. The biomimetic system is driven by a novel slip sensor, followed by slip perception and preventive control. Here, we show that biologically inspired sensorimotor pathways can be restored between the prosthetic [...] Read more.

This study develops biomimetic strategies for slip prevention in prosthetic hand grasps. The biomimetic system is driven by a novel slip sensor, followed by slip perception and preventive control. Here, we show that biologically inspired sensorimotor pathways can be restored between the prosthetic hand and users. A Ruffini endings-like slip sensor is used to detect shear forces and identify slip events directly. The slip information and grip force are encoded into a bi-state sensory coding that evokes vibration and buzz tactile sensations in subjects with transcutaneous electrical nerve stimulation (TENS). Subjects perceive slip events under various conditions based on the vibration sensation and voluntarily adjust grip force to prevent further slipping. Additionally, short-latency compensation for grip force is also implemented using a neuromorphic reflex pathway. The reflex loop includes a sensory neuron and interneurons to adjust the activations of antagonistic muscles reciprocally. The slip prevention system is tested in five able-bodied subjects and two transradial amputees with and without reflex compensation. A psychophysical test for perception reveals that the slip can be detected effectively, with a success accuracy of 96.57%. A slip protection test indicates that reflex compensation yields faster grasp adjustments than voluntary action, with a median response time of 0.30 (0.08) s, a rise time of 0.26 (0.03) s, an execution time of 0.56 (0.07) s, and a slip distance of 0.39 (0.10) cm. Prosthetic grip force is highly correlated to that of an intact hand, with a correlation coefficient of 96.85% (2.73%). These results demonstrate that it is feasible to reconstruct slip biomimetic sensorimotor pathways that provide grasp stability for prosthetic users. Full article

(This article belongs to the Special Issue Human-Inspired Grasp Control in Robotics)

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23 pages, 10315 KiB

Open AccessArticle

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 1517

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

Open AccessArticle

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

Cited by 1 | Viewed by 1221

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

Open AccessArticle

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

Cited by 2 | Viewed by 1321

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

Open AccessArticle

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

Cited by 1 | Viewed by 1354

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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