A Human-Robot Interaction Based Coordination Control Method for Assistive Walking Devices and an Assessment of Its Stability

Zhang, Xia; Wenliang, Ge; Fu, Hao; Chen, Renxiang; Luo, Tianhong; Hashimoto, Minoru

doi:10.1155/2018/9279627

Cited by 2 publications

(1 citation statement)

References 35 publications

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“…Therefore, many researchers are interested in achieving variable stiffness [71][72][73][74]. One method for this is to fill the muscles with high-mass coefficient liquids like water, but this increases the weight of these actuators' structures [75][76][77]. Most designs aiming to achieve variable stiffness depend on combining contractor and extensor muscles [54,78,79], where two actuators can oppose each other and thus control the stiffness.…”

Section: Introductionmentioning

confidence: 99%

A Novel Variable Stiffness Compound Extensor-Pneumatic Artificial Muscle (CE-PAM): Design and Mathematical Model

Al-Mayahi,

Al-Fahaam

2023

Journal of Robotics and Control

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Pneumatic artificial muscles (PAMs) have been exploited in robots utilized in various fields, including industry and medicine, due to their numerous advantages, such as their light weight; smooth, fast responses; and ability to generate significant force when fully extended. The actuator’s stiffness is important in these applications, and extensor PAMs (EPAMs) have a lower stiffness when compared to contractor PAMs (CPAMs). Because of this, this research presents the compound extensor PAM (CE-PAM), which is a novel actuator that has higher stiffness and can alter its stiffness at a fixed length or maintain a fixed stiffness at a variable length. This makes it useful in applications such as surgery robots and wearable robots. The CE-PAM is created by inserting the CPAM into the EPAM. Then, a mathematical model is developed to calculate the output force using several mathematical equations that relate the force, actuator size, and applied pressure to each other. The force is also calculated experimentally, and when comparing the mathematical with the experimental results, the error percentage appears greater than 20%. So the mathematical model is enhanced by calculating the wasted energy consumed by the actuator before the start of the bladder’s expansion, at which the force is zero because the pressure is consumed only for bladder expansion to touch the sleeve. The effect of the bladder’s thickness is calculated to further enhance the model by calculating the volume of air entering the muscle rather than the total muscle volume. To illustrate the effect of thickness on the actuator, experiments are conducted on CPAMs made of the same bladder material but with different thicknesses. A balloon is used in the manufacture of the bladder. Because it is a lightweight, thin material with a low thickness, it requires very low pressure to expand.

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Section: Introductionmentioning

confidence: 99%

A Novel Variable Stiffness Compound Extensor-Pneumatic Artificial Muscle (CE-PAM): Design and Mathematical Model

Al-Mayahi,

Al-Fahaam

2023

Journal of Robotics and Control

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Human–robot interaction: predicting research agenda by long short-term memory

Borregan-Alvarado,

Alvarez-Meaza,

Cilleruelo-Carrasco

et al. 2024

PeerJ Computer Science

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The article addresses the identification and prediction of research topics in human–robot interaction (HRI), fundamental in Industry 4.0 (I4.0) and future Industry 5.0 (I5.0). In the absence of research agendas in the scientific literature, the study proposes a multilayered model to create a precise agenda to guide the scientific community in new developments in collaborative robotics and HRI technologies. The methodology is divided into four stages, which make up the three layers of the model. In the first two stages, scientific articles on HRI for the period 2020–2021 were collected and analyzed using data mining techniques together with VantagePoint and Gephi software to identify keywords and their relationships. These initial stages form layer 1 of the model, where the main scientific themes are recognized. In the third stage, article titles and abstracts are cleaned and processed using natural language processing (NLP) techniques, generating word embeddings models that highlight relevant HRI-related terms, forming layer 2. The fourth and final stage uses Recurrent Neural Networks (RNN) with long short-term memory (LSTM) architecture to predict future topics, consolidating the previously identified terms and forming layer 3 of the model. The results show that in layer 1 HRI has intensive application in various sectors through advanced computational algorithms, with trust as a key feature. In layer 2, terms such as vision, sensors, communication, collaboration and anthropomorphic aspects are fundamental, while layer 3 anticipates future topics such as design, performance, method and controllers, essential to improve robot interaction. The study concludes that the methodology is effective in defining a robust and relevant research agenda. By identifying future trends and needs, this work fills a gap in the scientific literature, providing a valuable tool for the research community in the field of HRI.

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A Human-Robot Interaction Based Coordination Control Method for Assistive Walking Devices and an Assessment of Its Stability

Cited by 2 publications

References 35 publications

A Novel Variable Stiffness Compound Extensor-Pneumatic Artificial Muscle (CE-PAM): Design and Mathematical Model

A Novel Variable Stiffness Compound Extensor-Pneumatic Artificial Muscle (CE-PAM): Design and Mathematical Model

Human–robot interaction: predicting research agenda by long short-term memory

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