Sensor Fusion-based Anthropomorphic Control of Under-Actuated Bionic Hand in Dynamic Environment

Su, Hang; Zhang, Junhao; Fu, J.; Qi, Wen; Li, Guoxin; Hu, Yidan; Li, Zhijun

doi:10.1109/iros51168.2021.9636436

Cited by 12 publications

(9 citation statements)

References 22 publications

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“…In our study, although the system using time-domain features achieved high accuracy, a significant amount of time was also spent for different subjects to train the system for optimal setup, which was the same as reported in the literature. Using sensor fusion-based myoelectrical control (Su et al, 2021b ; Qi and Su, 2022 ) is a future direction, but these methods usually require a high computational power which is difficult for embedded systems to achieve.…”

Section: Discussionmentioning

confidence: 99%

A Real-Time EMG-Based Fixed-Bandwidth Frequency-Domain Embedded System for Robotic Hand

Chen

et al. 2022

Front. Neurorobot.

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The signals from electromyography (EMG) have been used for volitional control of robotic assistive devices with the challenges of performance improvement. Currently, the most common method of EMG signal processing for robot control is RMS (root mean square)-based algorithm, but system performance accuracy can be affected by noise or artifacts. This study hypothesized that the frequency bandwidths of noise and artifacts are beyond the main EMG signal frequency bandwidth, hence the fixed-bandwidth frequency-domain signal processing methods can filter off the noise and artifacts only by processing the main frequency bandwidth of EMG signals for robot control. The purpose of this study was to develop a cost-effective embedded system and short-time Fourier transform (STFT) method for an EMG-controlled robotic hand. Healthy volunteers were recruited in this study to identify the optimal myoelectric signal frequency bandwidth of muscle contractions. The STFT embedded system was developed using the STM32 microcontroller unit (MCU). The performance of the STFT embedded system was compared with RMS embedded system. The results showed that the optimal myoelectric signal frequency band responding to muscle contractions was between 60 and 80 Hz. The STFT embedded system was more stable than the RMS embedded system in detecting muscle contraction. Onsite calibration was required for RMS embedded system. The average accuracy of the STFT embedded system is 91.55%. This study presents a novel approach for developing a cost-effective and less complex embedded myoelectric signal processing system for robot control.

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Section: Discussionmentioning

confidence: 99%

A Real-Time EMG-Based Fixed-Bandwidth Frequency-Domain Embedded System for Robotic Hand

Chen

et al. 2022

Front. Neurorobot.

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“…Then, a teacher-student network with joint angle, consistency, and physical loss is trained to directly give angle joints of the Shadow Hand with only human hand depth input. Su et al ( 2021 ) propose multi-leap motion controllers and a Kalman filter-based adaptive fusion framework to achieve real-time control of an under-actuated bionic hand according to the bending angles of human fingers. After solving the retargeting problem, the recorded teleoperation data could be used as supervision data for imitation learning in Section 4.2.…”

Section: Learning-based Manipulation Methodsmentioning

confidence: 99%

“…Due to the development of neuroscience and information science as well as new materials and sensors, a series of robotic hands and their learning and control methods are designed and proposed. Among them, on the one hand, mimicking the perception, sensor-motor control and development structures, mechanisms and materials of the human hand is a promising way, such as flexible and stretchable skins, multimodal fusion, and synergy control (Gerratt et al, 2014 ; Ficuciello et al, 2019 ; Su et al, 2021 ). On the other hand, deep learning based representation learning, adaptive control concerning uncertainties, learning-based manipulation methods, such as deep convolutional neural network (CNN), reinforcement learning, imitation learning, and meta-learning (Rajeswaran et al, 2017 ; Yu et al, 2018 ; Li et al, 2019a ; Nagabandi et al, 2019 ; Su et al, 2020 ), show significant superiority for robotic movement and manipulation learning and adaptation.…”

Section: Introductionmentioning

confidence: 99%

A Survey of Multifingered Robotic Manipulation: Biological Results, Structural Evolvements, and Learning Methods

Wang

et al. 2022

Front. Neurorobot.

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Multifingered robotic hands (usually referred to as dexterous hands) are designed to achieve human-level or human-like manipulations for robots or as prostheses for the disabled. The research dates back 30 years ago, yet, there remain great challenges to effectively design and control them due to their high dimensionality of configuration, frequently switched interaction modes, and various task generalization requirements. This article aims to give a brief overview of multifingered robotic manipulation from three aspects: a) the biological results, b) the structural evolvements, and c) the learning methods, and discuss potential future directions. First, we investigate the structure and principle of hand-centered visual sensing, tactile sensing, and motor control and related behavioral results. Then, we review several typical multifingered dexterous hands from task scenarios, actuation mechanisms, and in-hand sensors points. Third, we report the recent progress of various learning-based multifingered manipulation methods, including but not limited to reinforcement learning, imitation learning, and other sub-class methods. The article concludes with open issues and our thoughts on future directions.

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“…Later, the Shadow Hand was manipulated through a network based on teacher-student with joint angle, consistency, and physical loss solely relying on human hand depth input. To accomplish realtime control of an under-actuated bionic hand in accordance with the bending angles of human fingers, Su et al [97] propose multi-leap motion controllers and a Kalman filter-based adaptive fusion architecture.…”

Section: Learning From Observation (Lfo)mentioning

confidence: 99%

“…To accomplish real‐time control of an under‐actuated bionic hand in accordance with the bending angles of human fingers, Su et al. [97] propose multi‐leap motion controllers and a Kalman filter‐based adaptive fusion architecture.…”

Section: Manipulationmentioning

confidence: 99%

Review on human‐like robot manipulation using dexterous hands

Sampath

Wang

Wu³

et al. 2023

Cognitive Comp and Systems

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In recent years, human hand‐based robotic hands or dexterous hands have gained attention due to their enormous capabilities of handling soft materials compared to traditional grippers. Back in the earlier days, the development of a hand model close to that of a human was an impossible task but with the advancements made in technology, dexterous hands with three, four or five‐fingered robotic hands have been developed to mimic human hand nature. However, human‐like manipulation of dexterous hands to this date remains a challenge. Thus, this review focuses on (a) the history and motivation behind the development of dexterous hands, (b) a brief overview of the available multi‐fingered hands, and (c) learning‐based methods such as traditional and data‐driven learning methods for manipulating dexterous hands. Additionally, it discusses the challenges faced in terms of the manipulation of multi‐fingered or dexterous hands.

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Sensor Fusion-based Anthropomorphic Control of Under-Actuated Bionic Hand in Dynamic Environment

Cited by 12 publications

References 22 publications

A Real-Time EMG-Based Fixed-Bandwidth Frequency-Domain Embedded System for Robotic Hand

A Real-Time EMG-Based Fixed-Bandwidth Frequency-Domain Embedded System for Robotic Hand

A Survey of Multifingered Robotic Manipulation: Biological Results, Structural Evolvements, and Learning Methods

Review on human‐like robot manipulation using dexterous hands

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