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Recent Advances in Medical Robots: Design and Applications
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Recent Advances in Medical Robots: Design and Applications

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

Deadline for manuscript submissions: 30 November 2026 | Viewed by 3214

Special Issue Editor

Dr. Zhenguo Nie

E-Mail Website
Guest Editor
Department of Mechanical Engineering, Tsinghua University, Beijing 100084, China
Interests: medical engineering; mechanical engineering; robotics; computer vision; AI

Special Issue Information

Dear Colleagues,

The robotization of surgeries and treatments has witnessed remarkable development. Nowadays, with the deepening of the integration of medicine and engineering in this field, an increasing number of surgical and treatment scenarios present new impetus and challenges for medical robots. The development of medical robots is facing new challenges, especially in terms of sensor technology. Advanced medical sensors, including force/torque sensors, optical sensors, and bio-sensors, serve as the nervous system of medical robots, enabling precise perception and real-time feedback during complex surgical procedures. These sensing capabilities are fundamental to achieving safe and effective human–robot collaboration.

In the face of more specialized and diversified surgical needs, more diverse perception methods and decision-making abilities are required. Currently, medical robots are making rapid progress in this field by leveraging cutting-edge sensor technologies such as haptic feedback systems, 3D vision sensors, and miniature in-body sensors. These innovations allow medical robots to not only be executors assisting physicians and therapists in treatment but also extensions of their eyes, hands, and even minds through enhanced sensory capabilities. The integration of multimodal sensor systems has made medical robots indispensable and crucial partners in the future intelligent medical system.

This Special Issue aims to explore more diversified perception and decision-making processes in medical robots, with particular emphasis on novel sensor technologies and their clinical applications. We will showcase the latest progress in medical robot design, including breakthroughs in sensor fusion algorithms, AI-enhanced sensory processing, and miniaturized implantable sensors. Articles focusing on intelligent perception based on artificial intelligence technology, data-driven intelligent control of robots, and efforts to optimize the design process of medical robots will also be included in this Special Issue. We expect that engineering applications can participate in the treatment process more efficiently, intelligently, and safely, thus bringing a higher quality of life to more people, not just patients.

Dr. Zhenguo Nie
Guest Editor

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 robot
  • multimodal sensing
  • intelligent sensing
  • haptic perception
  • human–robot interaction
  • control
  • teleoperation
  • robot design

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Published Papers (3 papers)

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Research

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13 pages, 12412 KB  
Article
A Real-Time Mechanical Information Acquisition System and Finite Element Prediction Method for Limb Lengthening: A Pilot In Vivo Study
by Hao Yang, Tairan Peng, Yuyang Han, Ming Lu, Yunzhi Chen and Zheng Yang
Sensors 2026, 26(6), 1950; https://doi.org/10.3390/s26061950 - 20 Mar 2026
Viewed by 475
Abstract
In the field of orthopedic surgery, particularly distraction osteogenesis (DO), the mechanical environment plays a decisive role in the quality of bone regeneration and the safety of the soft tissue envelope. The continuous monitoring and accurate prediction of distraction resisting forces (DRF) are [...] Read more.
In the field of orthopedic surgery, particularly distraction osteogenesis (DO), the mechanical environment plays a decisive role in the quality of bone regeneration and the safety of the soft tissue envelope. The continuous monitoring and accurate prediction of distraction resisting forces (DRF) are critical for preventing soft tissue complications such as nerve ischemia, joint contractures, and mechanical failure of the lengthening device. However, current clinical practice relies heavily on subjective assessment or passive monitoring tools that lack predictive capabilities. To address this gap, this study proposes a comprehensive solution combining a custom mechanical acquisition system with a high-fidelity finite element (FE) prediction method. The system design features a novel “double-ring” sensor interface specifically engineered to decouple axial distraction forces from parasitic bending moments generated by asymmetric muscle tension. Furthermore, a patient-specific FE model utilizing the Ogden hyperelastic constitutive law was derived, explicitly based on the patient’s muscle volume from preoperative CT imaging, to predict the non-linear force evolution. The feasibility and accuracy of the system were validated in a pilot in vivo study using a single ovine model (N=1). To isolate the soft tissue resistance from callus formation, distraction was performed immediately postoperatively up to a total length of 4 cm. Experimental results demonstrated the system’s high linearity (R2>0.999) and its ability to capture the characteristic viscoelastic relaxation of living tissues. The FE model successfully predicted the peak distraction forces, showing improved agreement with experimental data at larger distraction magnitudes. By integrating mechanical sensing with predictive modeling, this framework lays the foundation for future closed-loop, patient-specific control in distraction osteogenesis. Full article
(This article belongs to the Special Issue Recent Advances in Medical Robots: Design and Applications)
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17 pages, 3323 KB  
Article
Enhancing Torque Output for a Magnetic Actuation System for Robotic Spinal Distraction
by Yumei Li, Zikang Li, Ding Lu, Tairan Peng, Yunzhi Chen, Gang Fu, Zhenguo Nie and Fangyuan Wei
Sensors 2025, 25(20), 6497; https://doi.org/10.3390/s25206497 - 21 Oct 2025
Viewed by 1162
Abstract
Magnetically controlled spinal growing rods, used for treating early-onset scoliosis (EOS), face a critical clinical limitation: insufficient distraction force. Compounding this issue is the inherent inability to directly monitor the mechanical output of such implants in vivo, which challenges their safety and efficacy. [...] Read more.
Magnetically controlled spinal growing rods, used for treating early-onset scoliosis (EOS), face a critical clinical limitation: insufficient distraction force. Compounding this issue is the inherent inability to directly monitor the mechanical output of such implants in vivo, which challenges their safety and efficacy. To overcome these limitations, optimizing the rotor’s maximum torque is essential. Furthermore, defining the “continuous rotation domain” establishes a vital safety boundary for stable operation, preventing loss of synchronization and loss of control, thus safeguarding the efficacy of future clinical control strategies. In this study, a transient finite element magnetic field simulation model of a circumferentially distributed permanent magnet–rotor system was established using ANSYS Maxwell (2024). The effects of the clamp angle between the driving magnets and the rotor, the number of pole pairs, the rotor’s outer diameter, and the rotational speed of the driving magnets on the rotor’s maximum torque were systematically analyzed, and the optimized continuous rotation domain of the rotor was determined. The results indicated that when the clamp angle was set at 120°, the number of pole pairs was one, the rotor outer diameter was 8 mm, the rotor achieved its maximum torque and exhibited the largest continuous rotation domain, while the rotational speed of the driving magnets had no effect on maximum torque. Following optimization, the maximum torque of the simulation increased by 201% compared with the pre-optimization condition, and the continuous rotation domain was significantly enlarged. To validate the simulation, a rotor torque measurement setup incorporating a torque sensor was constructed. Experimental results showed that the maximum torque improved from 30 N·mm before optimization to 90 N·mm after optimization, while the driving magnets maintained stable rotation throughout the process. Furthermore, a spinal growing rod test platform equipped with a pressure sensor was developed to evaluate actuator performance and measure the maximum distraction force. The optimized growing rod achieved a peak distraction force of 413 N, nearly double that of the commercial MAGEC system, which reached only 208 N. The simulation and experimental methodologies established in this study not only optimizes the device’s performance but also provides a viable pathway for in vivo performance prediction and monitoring, addressing a critical need in smart implantable technology. Full article
(This article belongs to the Special Issue Recent Advances in Medical Robots: Design and Applications)
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Review

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35 pages, 1965 KB  
Review
A Review and Perspective of Techniques for Autonomous Robotic Ultrasound Acquisitions
by Yanding Qin, Lele Dang, Fan Ren, Zhuomao Li, Lijun Duan, Hongpeng Wang and Jianda Han
Sensors 2026, 26(7), 2081; https://doi.org/10.3390/s26072081 - 27 Mar 2026
Viewed by 846
Abstract
Ultrasound (US) imaging is a widely used diagnostic method in clinics. Real-time-generated US images are used for rapid diagnosis without harm to patients. The quality of US imaging highly depends on the skill of the physician due to the differences among physicians. Techniques [...] Read more.
Ultrasound (US) imaging is a widely used diagnostic method in clinics. Real-time-generated US images are used for rapid diagnosis without harm to patients. The quality of US imaging highly depends on the skill of the physician due to the differences among physicians. Techniques for autonomous robotic ultrasound (AU-RUS) acquisitions are expected to become an effective means to improve the level of US diagnosis, reduce the workload of physicians, and improve the standardization of US imaging quality. This paper aims to summarize the current research status of techniques for AU-RUS acquisitions, and to discuss the research trends and challenges regarding related technologies. Firstly, the techniques for AU-RUS acquisitions and systems are outlined. The techniques for teleoperated or autonomous US acquisitions are briefly discussed. Representative RUS acquisition systems are introduced. Then, the current research status of AU-RUS acquisitions is reviewed from four research directions: force sensitivity and control, scanning path-planning and positioning, US treatment guidance, and US image processing technology and quality assessment optimization. This review provides a decision-oriented autonomy perspective by mapping typical methods to workflow components across the stages of perception, decision-making, and execution. We identify major deployment bottlenecks, including safety-verifiable autonomy and failure recovery, motion compensation under deformation, and the lack of standardized, clinically meaningful US image quality metrics. Finally, the shortcomings of current research are summarized and analyzed, and the research trends and challenges for AU-RUS acquisitions are prospected. Full article
(This article belongs to the Special Issue Recent Advances in Medical Robots: Design and Applications)
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