Funabot-Suit: A bio-inspired and McKibben muscle-actuated suit for natural kinesthetic perception

Peng, Yanhong; Sakai, Yusuke; Nakagawa, Koki; Funabora, Yuki; Aoyama, Tadayoshi; Yokoe, Kenta; Doki, Shinji

doi:10.1016/j.birob.2023.100127

Cited by 13 publications

(6 citation statements)

References 47 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In addition to the above applications, Peng et al ( 2023 ) presents a novel approach to modeling soft fabric-type actuators using deep learning to correct simulated point clouds and improve their resemblance to real actuators. By employing PointNet and LSTM, the authors effectively capture time-series data and enhance the accuracy of point cloud features prediction, ultimately improving the similarity between corrected simulated point clouds and real data.…”

Section: Applicationsmentioning

confidence: 99%

A review of rigid point cloud registration based on deep learning

Chen,

Feng,

et al. 2024

Front. Neurorobot.

View full text Add to dashboard Cite

With the development of 3D scanning devices, point cloud registration is gradually being applied in various fields. Traditional point cloud registration methods face challenges in noise, low overlap, uneven density, and large data scale, which limits the further application of point cloud registration in actual scenes. With the above deficiency, point cloud registration methods based on deep learning technology gradually emerged. This review summarizes the point cloud registration technology based on deep learning. Firstly, point cloud registration based on deep learning can be categorized into two types: complete overlap point cloud registration and partially overlapping point cloud registration. And the characteristics of the two kinds of methods are classified and summarized in detail. The characteristics of the partially overlapping point cloud registration method are introduced and compared with the completely overlapping method to provide further research insight. Secondly, the review delves into network performance improvement summarizes how to accelerate the point cloud registration method of deep learning from the hardware and software. Then, this review discusses point cloud registration applications in various domains. Finally, this review summarizes and outlooks the current challenges and future research directions of deep learning-based point cloud registration.

show abstract

Section: Applicationsmentioning

confidence: 99%

A review of rigid point cloud registration based on deep learning

Chen,

Feng,

et al. 2024

Front. Neurorobot.

View full text Add to dashboard Cite

show abstract

“…One of the most accepted definitions for tensegrity systems is the one proposed by A. Pugh [2], where he defines them as a set of discontinuous elements subject to compression loads that interact with a set of continuous elements subject to tension loads, forming a stable volume in space; the discontinuous elements are called bars, and the continuous ones are denominated flexible elements. Incorporating various types of actuators, such as McKibben artificial muscles [3], which enable the variation of the length of flexible elements into a tensegrity structure, creates tensegrity robots. The actuators incorporated into tensegrity robots can be powered by various sources of motion, such as EHD pumps [4], piezoelectric materials [5], and cable-pulley systems [6], among others.…”

Section: Introductionmentioning

confidence: 99%

Novel Technique to Increase the Effective Workspace of a Soft Robot

Pérez-Soto,

Camarillo-Gómez,

Rodríguez-Reséndiz

et al. 2024

Micromachines

View full text Add to dashboard Cite

This article presents a novel technique for a class 2 tensegrity robot, also classified as a soft robot, to increase workspace by increasing the number of geometric equilibrium configurations of the robot. The proposed modification, unlike the strategies reported in the literature, consists of increasing the number of points where the flexible and rigid elements that make up the robot come into contact without the need to increase the number of actuators, the number of flexible elements, or modify the geometry of the rigid elements. The form-finding methodology combines the basic principles of statics with the direct and inverse kinematic position analysis to determine the number of equilibrium positions of the modified robot. In addition, numerical experiments were carried out using the commercial software ANSYS®, R18.2 based on the finite element theory, to corroborate the results obtained with them. With the proposed modification, an increase of 23.369% in the number of geometric equilibrium configurations is achieved, which integrates the workspace of the modified class 2 tensegrity robot. The novel technique applied to tensegrity robots and the tools developed to increase their workspace apply perfectly to scale the robots presented in this paper.

show abstract

“…These innovations are driving progress and development in diverse industries. For instance, in the realm of robotics, Peng et al introduced the Funabot-Suit, a biologically inspired garment propelled by McKibben muscles, enabling natural proprioceptive perception [2]. Meanwhile, Mao et al devised a predictive modeling approach for flexible electro-hydrodynamic pumps, leveraging soft computing techniques [3].…”

Section: Introductionmentioning

confidence: 99%

A Novel Intelligent Fault Diagnosis Method for Self-Priming Centrifugal Pumps

Zhang,

Wang,

Yao

et al. 2023

Entropy

View full text Add to dashboard Cite

The real-time diagnostic monitoring of self-priming centrifugal pumps is essential to ensure their safe operation. Nevertheless, owing to the intricate structure and complex operational conditions inherent in such pumps, existing fault diagnosis methods encounter challenges in effectively extracting crucial fault feature information and accurately identifying fault types. Consequently, this paper introduces an intelligent fault diagnosis method tailored for self-priming centrifugal pumps. The approach amalgamates refined time-shift multiscale fluctuation dispersion entropy, cosine pairwise-constrained supervised manifold mapping, and adaptive chaotic Aquila optimization support vector machine techniques. To begin with, refined time-shift multiscale fluctuation dispersion entropy is employed to extract fault-related features, adeptly mitigating concerns related to entropy domain deviations and instability. Subsequently, the application of cosine pairwise-constrained supervised manifold mapping serves to reduce the dimensionality of the extracted fault features, thereby bolstering the efficiency and precision of the ensuing identification process. Ultimately, the utilization of an adaptive chaotic Aquila optimization support vector machine facilitates intelligent fault classification, leading to enhanced accuracy in fault identification. The experimental findings unequivocally affirm the efficacy of the proposed method in accurately discerning among various fault types in self-priming centrifugal pumps, achieving an exceptional recognition rate of 100%. Moreover, it is noteworthy that the average correct recognition rate achieved by the proposed method surpasses that of five existing intelligent fault diagnosis techniques by a significant margin, registering a notable increase of 15.97%.

show abstract

Funabot-Suit: A bio-inspired and McKibben muscle-actuated suit for natural kinesthetic perception

Cited by 13 publications

References 47 publications

A review of rigid point cloud registration based on deep learning

A review of rigid point cloud registration based on deep learning

Novel Technique to Increase the Effective Workspace of a Soft Robot

A Novel Intelligent Fault Diagnosis Method for Self-Priming Centrifugal Pumps

Contact Info

Product

Resources

About