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Advanced Sensors and AI Integration for Human–Robot Teaming
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Advanced Sensors and AI Integration for Human–Robot Teaming

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

Deadline for manuscript submissions: 25 January 2026 | Viewed by 2507

Special Issue Editors

Prof. Dr. Nader Jalili

E-Mail Website
Guest Editor
Mary and Richard Templeton Dean of Lyle School of Engineering, Professor of Mechanical Engineering, Southern Methodist University (SMU), Dallas, TX, USA
Interests: automation; human–robot collaboration/integration and augmentation; micro nano-electromechanical sensors and actuators; smart manufacturing systems; human motor control; EEG; machine learning
Dr. Soroush Korivand

E-Mail Website
Guest Editor
Michael W. Hall School of Mechanical Engineering, Mississippi State University, Starkville, Starkville, MS, USA
Interests: human–robot interaction; reinforcement learning; AI in rehabilitation

Special Issue Information

This Special Issue on "Advanced Sensors and AI Integration for Human–Robot Teaming" aims to explore the latest advancements in sensor technologies and their integration with artificial intelligence (AI) to enhance human–robot collaboration. As the fields of robotics and AI continue to evolve, the need for sophisticated sensors that can provide precise, real-time data is paramount. This issue will cover innovative sensor designs, data acquisition methods, and AI algorithms that enable seamless interaction between humans and robots.

This Special Issue invites contributions that address the challenges and opportunities in developing sensors that are not only accurate and reliable but also capable of being integrated into complex AI systems. Topics of interest include, but are not limited to, sensor fusion, machine learning for sensor data interpretation, real-time data processing, and the use of sensors in autonomous systems. By bringing together research from these diverse areas, this issue aims to provide a comprehensive overview of how advanced sensor technologies and AI can work together to improve the efficiency, safety, and functionality of human–robot teams.

Prof. Dr. Nader Jalili
Dr. Soroush Korivand
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 submissions that pass pre-check are 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 2600 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

  • human–robot teaming
  • sensor fusion
  • AI integration
  • real-time data processing
  • machine learning
  • autonomous systems
  • robotics
  • data acquisition
  • sensor technologies
  • intelligent systems

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (2 papers)

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Research

16 pages, 5201 KiB  
Article
Robotic Fast Patch Clamp in Brain Slices Based on Stepwise Micropipette Navigation and Gigaseal Formation Control
by Jinyu Qiu, Qili Zhao, Ruimin Li, Yuzhu Liu, Biting Ma and Xin Zhao
Sensors 2025, 25(4), 1128; https://doi.org/10.3390/s25041128 - 13 Feb 2025
Viewed by 590
Abstract
The patch clamp technique has become the gold standard for neuron electrophysiology research in brain science. Brain slices have been widely utilized as the targets of the patch clamp technique due to their higher optical transparency compared to a live brain and their [...] Read more.
The patch clamp technique has become the gold standard for neuron electrophysiology research in brain science. Brain slices have been widely utilized as the targets of the patch clamp technique due to their higher optical transparency compared to a live brain and their intercellular connectivity in comparison to cultured single neurons. However, the narrow working space, small scope, and depth of the field of view make the positioning of the operation’s micropipette to the target neuron a time-consuming task reliant on a high level of experience, significantly slowing down operation of the patch clamp technique in brain slices. Further, the current poor controllability in gigaseal formation, which is the key to electrophysiology signal recording, significantly lowers the patch clamp success rate. In this paper, a stepwise navigation of the micropipette is conducted to accelerate the positioning process of the micropipette tip to the target neuron in the brain slice. Then, a fuzzy proportional–integral–derivative controller is designed to control the gigaseal formation process along a designed resistance curve. The experimental results demonstrate an almost doubled patch clamp technique speed, with a 25% improvement in the success rate compared to the conventional manual method. The above advantages may promote the application of our method in brain science research based on brain slice platforms. Full article
(This article belongs to the Special Issue Advanced Sensors and AI Integration for Human–Robot Teaming)
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23 pages, 4654 KiB  
Article
Effective Acoustic Model-Based Beamforming Training for Static and Dynamic Hri Applications
by Alejandro Luzanto, Nicolás Bohmer, Rodrigo Mahu, Eduardo Alvarado, Richard M. Stern and Néstor Becerra Yoma
Sensors 2024, 24(20), 6644; https://doi.org/10.3390/s24206644 - 15 Oct 2024
Viewed by 1493
Abstract
Human–robot collaboration will play an important role in the fourth industrial revolution in applications related to hostile environments, mining, industry, forestry, education, natural disaster and defense. Effective collaboration requires robots to understand human intentions and tasks, which involves advanced user profiling. Voice-based communication, [...] Read more.
Human–robot collaboration will play an important role in the fourth industrial revolution in applications related to hostile environments, mining, industry, forestry, education, natural disaster and defense. Effective collaboration requires robots to understand human intentions and tasks, which involves advanced user profiling. Voice-based communication, rich in complex information, is key to this. Beamforming, a technology that enhances speech signals, can help robots extract semantic, emotional, or health-related information from speech. This paper describes the implementation of a system that provides substantially improved signal-to-noise ratio (SNR) and speech recognition accuracy to a moving robotic platform for use in human–robot interaction (HRI) applications in static and dynamic contexts. This study focuses on training deep learning-based beamformers using acoustic model-based multi-style training with measured room impulse responses (RIRs). The results show that this approach outperforms training with simulated RIRs or matched measured RIRs, especially in dynamic conditions involving robot motion. The findings suggest that training with a broad range of measured RIRs is sufficient for effective HRI in various environments, making additional data recording or augmentation unnecessary. This research demonstrates that deep learning-based beamforming can significantly improve HRI performance, particularly in challenging acoustic environments, surpassing traditional beamforming methods. Full article
(This article belongs to the Special Issue Advanced Sensors and AI Integration for Human–Robot Teaming)
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