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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: 31 December 2021.

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 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

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

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 2200 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 (1 paper)

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Research

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
Viewed by 488
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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