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Article
Publication date: 6 August 2024

Baoxu Tu, Yuanfei Zhang, Wangyang Li, Fenglei Ni and Minghe Jin

The aim of this paper is to enhance the control performance of dexterous hands, enabling them to handle the high data flow from multiple sensors and to meet the deployment…

Abstract

Purpose

The aim of this paper is to enhance the control performance of dexterous hands, enabling them to handle the high data flow from multiple sensors and to meet the deployment requirements of deep learning methods on dexterous hands.

Design/methodology/approach

A distributed control architecture was designed, comprising embedded motion control subsystems and a host control subsystem built on ROS. The design of embedded controller state machines and clock synchronization algorithms ensured the stable operation of the entire distributed control system.

Findings

Experiments demonstrate that the entire system can operate stably at 1KHz. Additionally, the host can accomplish learning-based estimates of contact position and force.

Originality/value

This distributed architecture provides foundational support for the large-scale application of machine learning algorithms on dexterous hands. Dexterity hands utilizing this architecture can be easily integrated with robotic arms.

Details

Industrial Robot: the international journal of robotics research and application, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 0143-991X

Keywords

Article
Publication date: 30 April 2024

Baoxu Tu, Yuanfei Zhang, Kang Min, Fenglei Ni and Minghe Jin

This paper aims to estimate contact location from sparse and high-dimensional soft tactile array sensor data using the tactile image. The authors used three feature extraction…

Abstract

Purpose

This paper aims to estimate contact location from sparse and high-dimensional soft tactile array sensor data using the tactile image. The authors used three feature extraction methods: handcrafted features, convolutional features and autoencoder features. Subsequently, these features were mapped to contact locations through a contact location regression network. Finally, the network performance was evaluated using spherical fittings of three different radii to further determine the optimal feature extraction method.

Design/methodology/approach

This paper aims to estimate contact location from sparse and high-dimensional soft tactile array sensor data using the tactile image.

Findings

This research indicates that data collected by probes can be used for contact localization. Introducing a batch normalization layer after the feature extraction stage significantly enhances the model’s generalization performance. Through qualitative and quantitative analyses, the authors conclude that convolutional methods can more accurately estimate contact locations.

Originality/value

The paper provides both qualitative and quantitative analyses of the performance of three contact localization methods across different datasets. To address the challenge of obtaining accurate contact locations in quantitative analysis, an indirect measurement metric is proposed.

Details

Industrial Robot: the international journal of robotics research and application, vol. 51 no. 5
Type: Research Article
ISSN: 0143-991X

Keywords

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