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Sensing and Control Technology of Intelligent Robots
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Sensing and Control Technology of Intelligent Robots

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

Deadline for manuscript submissions: 25 August 2026 | Viewed by 1922

Special Issue Editors

Dr. Vladimir Gurau

E-Mail Website1 Website2
Guest Editor
Department of Manufacturing Engineering, Georgia Southern University, Statesboro, GA 30458, USA
Interests: industrial robots; computer vision; machine learning; robotic vision inspection; algorithms for autonomous vehicle navigation; advanced manufacturing
Special Issues, Collections and Topics in MDPI journals
Dr. Doyun Lee

E-Mail Website
Guest Editor
Department of Civil Engineering and Construction, Georgia Southern University, Statesboro, GA 30458, USA
Interests: robot-to-robot collaboration; real-time sensing and feedback control systems for mobile robots; computer vision and machine learning for intelligent robotics; SLAM (Simultaneous Localization and Mapping) and 3D reconstruction for autonomous systems; robotic systems for automated inspections, monitoring and material handling

Special Issue Information

Dear Colleagues,

Robots have traditionally been operated in fixed, structured workspaces to carry out predetermined, repetitive tasks. Sensors integrated with machine learning (ML) and artificial intelligence (AI) technologies empower industrial robots with human-like reasoning capabilities that enable them to adapt to changes more autonomously, learn from real-time data and determine the best course of action. AI and ML-enabled industrial and collaborative robots are excellent at analyzing data, identifying patterns, and making well-informed decisions. They have enhanced task efficiency, can learn from experience, and adapt to changing work scenarios through data-driven learning.

This Special Issue aims to gather original research and review articles on recent advances, technologies, solutions, applications, and new challenges in the sensors and control technology of intelligent robots.

Potential topics include but are not limited to:

  • Robotic perception
  • Adaptive robotics
  • Machine vision application in robotics
  • Advanced robotic technologies
  • ML and AI-integrated sensor applications in robotics
  • Robot sensing
  • Robot control 

Dr. Vladimir Gurau
Dr. Doyun Lee
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 250 words) can be sent to the Editorial Office for assessment.

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

  • robot sensing
  • robot perception
  • adaptive robotics
  • real-time sensing
  • feedback control

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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

19 pages, 4114 KB  
Article
Formative Evaluation of Safety and Usability of a Mixed-Reality Robot-Assisted Telerehabilitation System for Post-Stroke Upper-Limb Therapy
by Md Mahafuzur Rahaman Khan, Kishor Lakshminarayanan, Inga Wang, Jennifer Barber, Erin M. McGonigle Ketchum and Mohammad H. Rahman
Sensors 2026, 26(10), 3043; https://doi.org/10.3390/s26103043 - 12 May 2026
Viewed by 273
Abstract
Robot-assisted telerehabilitation (RAT) combines rehabilitation robotics with digital health workflows to extend access to upper-limb (UL) therapy after stroke. Mixed reality (MR) may support therapist–patient interaction and task visualization; however, early-stage systems require rigorous evaluation of safety and usability before deployment in the [...] Read more.
Robot-assisted telerehabilitation (RAT) combines rehabilitation robotics with digital health workflows to extend access to upper-limb (UL) therapy after stroke. Mixed reality (MR) may support therapist–patient interaction and task visualization; however, early-stage systems require rigorous evaluation of safety and usability before deployment in the home. In a formative, mixed-methods usability study conducted in a controlled setting using a telerehabilitation workflow, six individuals post-stroke (≥3 months) and six occupational therapists (OTs) completed a single supervised session with a desktop-mounted end-effector type therapeutic robot (iTbot) integrated with Microsoft HoloLens 2. Participants performed structured passive and active UL exercises while therapists supervised and interacted with the system via the MR control interfaces. Safety was evaluated by documenting observed adverse events and safety-stop activations. Usability and user experience were assessed using the System Usability Scale (SUS), study-specific satisfaction questionnaires (reported with scale ranges), and semi-structured follow-up interviews analyzed using thematic analysis. All participants completed the session without observed adverse events or safety-stop activations. Overall usability was favorable, with a mean (SD) SUS total score of 78.3 (15.9) out of 100 (stroke: 74.2 [18.1]; occupational therapists: 82.5 [13.5]). Qualitative feedback indicated that MR was perceived as engaging and intuitive by many users, while also identifying implementation needs relevant to real-world telerehabilitation, including clearer onboarding, simplification of certain MR interactions, and improved physical interfaces (e.g., handle options). Therapists highlighted workflow considerations for remote supervision and patient independence. Together, these findings support progression to multi-session, in-home studies to quantify remote assistance needs, technical reliability, adherence, and clinical outcomes. Full article
(This article belongs to the Special Issue Sensing and Control Technology of Intelligent Robots)
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28 pages, 9411 KB  
Article
A Real-Time Mobile Robotic System for Crack Detection in Construction Using Two-Stage Deep Learning
by Emmanuella Ogun, Yong Ann Voeurn and Doyun Lee
Sensors 2026, 26(2), 530; https://doi.org/10.3390/s26020530 - 13 Jan 2026
Viewed by 1093
Abstract
The deterioration of civil infrastructure poses a significant threat to public safety, yet conventional manual inspections remain subjective, labor-intensive, and constrained by accessibility. To address these challenges, this paper presents a real-time robotic inspection system that integrates deep learning perception and autonomous navigation. [...] Read more.
The deterioration of civil infrastructure poses a significant threat to public safety, yet conventional manual inspections remain subjective, labor-intensive, and constrained by accessibility. To address these challenges, this paper presents a real-time robotic inspection system that integrates deep learning perception and autonomous navigation. The proposed framework employs a two-stage neural network: a U-Net for initial segmentation followed by a Pix2Pix conditional generative adversarial network (GAN) that utilizes adversarial residual learning to refine boundary accuracy and suppress false positives. When deployed on an Unmanned Ground Vehicle (UGV) equipped with an RGB-D camera and LiDAR, this framework enables simultaneous automated crack detection and collision-free autonomous navigation. Evaluated on the CrackSeg9k dataset, the two-stage model achieved a mean Intersection over Union (mIoU) of 73.9 ± 0.6% and an F1-score of 76.4 ± 0.3%. Beyond benchmark testing, the robotic system was further validated through simulation, laboratory experiments, and real-world campus hallway tests, successfully detecting micro-cracks as narrow as 0.3 mm. Collectively, these results demonstrate the system’s potential for robust, autonomous, and field-deployable infrastructure inspection. Full article
(This article belongs to the Special Issue Sensing and Control Technology of Intelligent Robots)
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