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Special Issue "Biologically Inspired Robotic Mechanisms, Control, and Multimodal Sensor Fusion for Human-Robot Coexistence"

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Sensors and Robotics".

Deadline for manuscript submissions: closed (30 June 2022) | Viewed by 2923

Special Issue Editors

Prof. Dr. Woosung Yang
E-Mail Website
Guest Editor
School of Robotics, Kwangwoon University, Seoul 01897, Korea
Interests: biologically inspired robot and control; multi-DOF robotic systems; humanoids; wearable robots; human–robot interaction system and control
Prof. Dr. Juhoon Back
E-Mail Website
Guest Editor
School of Robotics, Kwangwoon University, Seoul 01897, Korea
Interests: disturbance observer; robust control; model predictive control; robot manipulator; optimization; nonlinear control; estimation
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Robotic applications involving interactions between multi-DOF robotic systems and robot–environment and human–robot interactions play a more significant role in our daily lives that ever before and expanding rapidly. Biologically inspired robotic techniques to overcome existing limitations foster the dissemination of new robotic discoveries and applications that help and support humans. Although great progress has been achieved in recent years, there are still open issues due to the limitations in technology combined with insufficient knowledge about humans. Designing innovative robotic mechanisms, developing efficient and reliable control strategies, and realizing new cognitive sensor systems akin to humans or animals can be key technologies to advance current robot technologies. In addition, in recent years, applying data-driven technologies such as machine learning for perception and reinforcement learning for control to unexplored problems is another promising research direction.

Attention in this Special Issue is particularly focused on the various integrations of robotics with biologically inspired mechatronics systems and control technology, and cognitive sensor systems with the goal of developing new multidimensional robotic service. This includes mechanism modeling and design, intelligent control, cognitive algorithm and sensor fusion, robot learning, and cognitive modeling for human–robot collaboration.

Prof. Dr. Woosung Yang
Prof. Dr. Juhoon Back
Guest Editors

Manuscript Submission Information

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Design, modeling, and control of multi-DOF robotic systems
  • Biologically inspired mechanisms and applications
  • Development of biomechatronic systems for human-centered robots
  • Human-in-loop cooperative robotic systems and compliant control
  • Human intention and disturbance estimation
  • Vision, sensing, perception, and navigation
  • Collaborative robots and human–robot interaction
  • Control of multiple robots and network systems
  • Artificial intelligence, machine learning
  • Multimodal senser system

Published Papers (5 papers)

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Research

Article
Design of Under-Actuated Soft Adhesion Actuators for Climbing Robots
Sensors 2022, 22(15), 5639; https://doi.org/10.3390/s22155639 - 28 Jul 2022
Viewed by 177
Abstract
Since climbing robots mainly rely on adhesion actuators to achieve adhesion, robust adhesion actuators have always been the challenge of climbing robot design. A novel under-actuated soft adhesion actuator (USAA) proposed in this paper for climbing robots can generate adhesion through robot’s load [...] Read more.
Since climbing robots mainly rely on adhesion actuators to achieve adhesion, robust adhesion actuators have always been the challenge of climbing robot design. A novel under-actuated soft adhesion actuator (USAA) proposed in this paper for climbing robots can generate adhesion through robot’s load applied to the actuator. The actuator is composed of a soft film/substrate structure with an annular groove on the substrate and a cavity on the soft film. To fabricate the actuator, we first study the influence of the geometric parameters of the USAA on the maximum adhesion of the actuator by analysis and experiments, and then combine these parameters and the boundary conditions of the static meniscus in the mold to design the mold. Moreover, we fabricate a climbing robot equipped with USAAs and evaluate its performance on horizontal and inclined surfaces with a wide range of characteristics. The USAA can generate strong and controllable adhesion to various smooth and semi-smooth surfaces. Furthermore, the fabricated robot performs well on various surfaces under a certain load (at least 500 g) and speed (369 mm/min) through experiments. It’s adaptability to a variety of surfaces enables a wide range of applications and pushes the boundaries of soft adhesion actuators. Full article
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Article
Electrical Optimization Method Based on a Novel Arrangement of the Magnetic Navigation System with Gradient and Uniform Saddle Coils
Sensors 2022, 22(15), 5603; https://doi.org/10.3390/s22155603 - 27 Jul 2022
Viewed by 221
Abstract
The magnetic navigation system (MNS) with gradient and uniform saddle coils is an effective system for manipulating various medical magnetic robots because of its compact structure and the uniformity of its magnetic field and field gradient. Since each coil of the MNS was [...] Read more.
The magnetic navigation system (MNS) with gradient and uniform saddle coils is an effective system for manipulating various medical magnetic robots because of its compact structure and the uniformity of its magnetic field and field gradient. Since each coil of the MNS was geometrically optimized to generate strong uniform magnetic field or field gradient, it is considered that no special optimization is required for the MNS. However, its electrical characteristics can be still optimized to utilize the maximum power of a power supply unit with improved operating time and a stronger time-varying magnetic field. Furthermore, the conventional arrangement of the coils limits the maximum three-dimensional (3D) rotating magnetic field. In this paper, we propose an electrical optimization method based on a novel arrangement of the MNS. We introduce the objective functions, constraints, and design variables of the MNS considering electrical characteristics such as resistance, current density, and inductance. Then, we design an MNS using an optimization algorithm and compare it with the conventional MNS; the proposed MNS generates a magnetic field or field gradient 22% stronger on average than that of the conventional MNS with a sevenfold longer operating time limit, and the maximum three-dimensional rotating magnetic field is improved by 42%. We also demonstrate that the unclogging performance of the helical robot improves by 54% with the constructed MNS. Full article
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Article
Development of Wrist Interface Based on Fully Actuated Coaxial Spherical Parallel Mechanism for Force Interaction
Sensors 2021, 21(23), 8073; https://doi.org/10.3390/s21238073 - 02 Dec 2021
Viewed by 525
Abstract
To develop a wrist robotic exoskeleton-type interface (REI) for force interaction, it should have a suitable range of motion similar to human wrist activities of daily living, large torque output performance, and low moving parts inertia for dynamic motion response to cover the [...] Read more.
To develop a wrist robotic exoskeleton-type interface (REI) for force interaction, it should have a suitable range of motion similar to human wrist activities of daily living, large torque output performance, and low moving parts inertia for dynamic motion response to cover the human behavior frequency. In this paper, a wrist REI based on a fully actuated coaxial spherical parallel mechanism (CSPM) is proposed to satisfy the aforementioned features. The fully actuated CSPM-based wrist REI (FC-WREI) has the characteristics of pure rotation similar to the human wrist, high torque output by parallel torque synthesis, and low moving parts inertia due to the base arrangement of the actuators. Due to the mechanical advantages and design optimization, the FC-WREI maximally provides torque as much as 56.49–130.43% of the maximum isometric torque of the human wrist, while providing a consistent range of motion to the human wrist without interference problem. Moreover, it is confirmed that the inertia of the FC-WREI is up to 5.35 times lower than similar devices. These advantages of the FC-WREI mean that the device is applicable to various fields of REIs for force interaction. Full article
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Article
A Development of the Self Shape Adjustment Cushion Mechanism for Improving Sitting Comfort
Sensors 2021, 21(23), 7959; https://doi.org/10.3390/s21237959 - 29 Nov 2021
Viewed by 583
Abstract
The seat comfort of automobiles is one of the significant factors for determining the driver’s fatigue, emotional experience, and individual space (which captures their individuality, rather than just a means of transportation in modern society). Conventional automobile seats could not provide seating comfort [...] Read more.
The seat comfort of automobiles is one of the significant factors for determining the driver’s fatigue, emotional experience, and individual space (which captures their individuality, rather than just a means of transportation in modern society). Conventional automobile seats could not provide seating comfort suitable for all drivers, in the form of seats that fit each driver’s body type and the difficulty of meeting individual needs. This study proposes self-shape adjustable (the SSA seats) seats that improve the sitting comfort, safety, and secure the stability, by adjusting shape fit to the driver’s body type. The SSA seats transforms the seat itself, in a way that improves the distribution of contact pressure and reduces sitting fatigue, with the pneumatic system. The transformed seats provide better sitting comfort and safety than the conventional automobile seat, by providing a seat shape suitable for the body shape of all users. It was verified that the SSA seats, proposed in this paper, have a uniform and improved pressure distribution, compared to the conventional seat, in various sitting postures; the contact area between the seat and user is enlarged, and the pressure concentrated on the ischial bone is lowered. In addition, it was proven (through user evaluation) that quantitative evaluation verification was the same as qualitative evaluation results. Full article
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Communication
Movement Path Data Generation from Wi-Fi Fingerprints for Recurrent Neural Networks
Sensors 2021, 21(8), 2823; https://doi.org/10.3390/s21082823 - 16 Apr 2021
Cited by 2 | Viewed by 828
Abstract
The recurrent neural network (RNN) model, which is a deep-learning network that can memorize past information, is used in this paper to memorize continuous movements in indoor positioning to reduce positioning error. To use an RNN model in Wi-Fi-fingerprint based indoor positioning, data [...] Read more.
The recurrent neural network (RNN) model, which is a deep-learning network that can memorize past information, is used in this paper to memorize continuous movements in indoor positioning to reduce positioning error. To use an RNN model in Wi-Fi-fingerprint based indoor positioning, data set must be sequential. However, Wi-Fi fingerprinting only saves the received signal strength indicator for a location, so it cannot be used as RNN data. For this reason, we propose a movement path data generation technique that generates data for an RNN model for sequential positioning from Wi-Fi fingerprint data. Movement path data can be generated by creating an adjacency list for Wi-Fi fingerprint location points. However, creating an adjacency matrix for all location points requires a large amount of computation. This problem is solved by dividing indoor environment by K-means clustering and creating a cluster transition matrix based on the center of each cluster. Full article
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