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Sensors
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Novel Actuation, Sensing, and Intelligent Control in Medical Robotic Systems
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Novel Actuation, Sensing, and Intelligent Control in Medical Robotic Systems

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

Deadline for manuscript submissions: 15 November 2026 | Viewed by 1169

Special Issue Editors

Dr. Changyan He

E-Mail Website
Guest Editor
School of Engineering, University of Newcastle, Newcastle 2308, Australia
Interests: surgical robotics; magnetic actuation; capsule robots; continuum robots; FBG sensors; instrumentation; adaptive control
Dr. Majid Roshanfar

E-Mail Website
Guest Editor
The Wilfred and Joyce Posluns Centre for Image Guided Innovation & Therapeutic Intervention (PCIGITI), Hospital for Sick Children (SickKids), Toronto, ON M5G 1X8, Canada
Interests: continuum robotics; soft robotics; surgical robotics; medical robotics
Special Issues, Collections and Topics in MDPI journals
Dr. Leo Wu

E-Mail Website
Guest Editor
School of Mechanical and Manufacturing Engineering, University of New South Wales, Sydney, NSW 2052, Australia
Interests: medical robotics; assistive robots and technology; manufacturing robotics; intelligent robotics; human–computer interaction; control engineering; mechatronics and robotics; simulation; modelling; programming of mechatronics systems

Special Issue Information

Dear Colleagues,

Medical robotic systems are transforming modern healthcare by enabling precise, minimally invasive interventions and improving patient outcomes. This Special Issue focuses on recent advances in actuation technologies, sensing modalities, and intelligent control strategies that could empower the next generation of medical robots. From manipulator design and novel actuation methods to AI-guided control, emerging innovations are addressing long-standing clinical challenges such as safe navigation in constrained environments, real-time tissue interaction feedback, and autonomous decision-making during procedures. We welcome the submission of original research and review articles that explore novel actuation designs, sensor fusion techniques, AI-enhanced control algorithms, and real-world clinical validations.

Dr. Changyan He
Dr. Majid Roshanfar
Dr. Leo Wu
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

  • medical robotics
  • smart sensors
  • magnetic actuation
  • sensor fusion
  • AI in surgical control
  • soft robotics
  • continuum robots
  • flexible sensors
  • image-guided navigation
  • human–robot interaction

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Published Papers (1 paper)

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Research

32 pages, 32711 KB  
Article
Adaptive Control of the Redundant Axis of a Surgical Robot for Operating Room Workspace Optimization Using Reinforcement Learning
by Irati Renedo-Alonso, Juan A. Sánchez-Margallo, Nestor Arana-Arexolaleiba and Íñigo Elguea-Aguinaco
Sensors 2026, 26(9), 2881; https://doi.org/10.3390/s26092881 - 5 May 2026
Viewed by 701
Abstract
Laparoscopy is one of the most widely used surgical techniques in clinical practice. However, its practice is associated with medium- and long-term musculoskeletal disorders in surgeons. In this context, robot-assisted surgery has emerged as a promising approach for mitigating ergonomic constraints while enhancing [...] Read more.
Laparoscopy is one of the most widely used surgical techniques in clinical practice. However, its practice is associated with medium- and long-term musculoskeletal disorders in surgeons. In this context, robot-assisted surgery has emerged as a promising approach for mitigating ergonomic constraints while enhancing control and precision during laparoscope manipulation. Despite these advances, existing research predominantly focuses on robotic control strategies, whereas the study of human–robot interaction in the operating room remains comparatively underexplored. This paper presents a proof-of-concept framework for workspace-aware posture adaptation in collaborative surgical robotics. The proposed approach combines vision-based human activity recognition with reinforcement learning to control the shoulder–elbow–wrist redundant angle of a seven-degree-of-freedom manipulator holding a laparoscope. Based on the detected interaction context, the system distinguishes between controlling, observing, cutting, and blocked states. During the observation and cutting phases, the controller allows the robot’s posture to be reconfigured so that it tilts away from the human operator while maintaining the position of the laparoscope; when the surgeon moves away, the robot gradually returns to its default configuration. Two reward formulations, dense and fuzzy, are compared. Real-world experiments show that both approaches learn the desired reflexive behavior, while the fuzzy reward yields improved training stability and more consistent real-system performance, increasing workspace availability around the surgeon. Full article
(This article belongs to the Special Issue Novel Actuation, Sensing, and Intelligent Control in Medical Robotic Systems)
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