Robotic System Research Articles

Assessing the feasibility and acceptability of a hands-on surgical robotics workshop for medical students and early-career doctors.

Despite the increased adoption of robotic surgery across various specialities, medical students and early-career doctors have limited exposure. This study aims to assess the feasibility and acceptability of a hands-on surgical robotics workshop for those early-career medics. 26 participants with minimal prior exposure to robotic surgery attended a workshop using the Versius© robotic surgical system and Virtual Reality simulation platforms. We analysed pre-workshop registration form and post-workshop feedback form utilising mixed quantitative and qualitative approach. Pre-workshop registration revealed motivations and barriers to attending surgical robotics courses. Post-workshop evaluations showed significant improvements in self-assessment scores, familiarity with robotic surgery, and confidence in using the technology. All participants expressed a strong enthusiasm for greater access to robotic surgery education and 84% of participants strongly agreed this workshop increased their interests in surgical specialities. The study also highlighted the perceived ease of use of robotic systems compared to laparoscopic instruments and explored the potential of virtual reality in surgical training. Further efforts are needed for better integration of robotic surgery training into medical curricula to prepare future surgeons for the evolving surgical landscape.

Predicting Continuous Locomotion Modes via Multidimensional Feature Learning From sEMG.

Walking-assistive devices require adaptive control methods to ensure smooth transitions between various modes of locomotion. For this purpose, detecting human locomotion modes (e.g., level walking or stair ascent) in advance is crucial for improving the intelligence and transparency of such robotic systems. This study proposes Deep-STF, a unified end-to-end deep learning model designed for integrated feature extraction in spatial, temporal, and frequency dimensions from surface electromyography (sEMG) signals. Our model enables accurate and robust continuous prediction of nine locomotion modes and 15 transitions at varying prediction time intervals, ranging from 100 to 500 ms. Experimental results showcased Deep-STP's cutting-edge prediction performance across diverse locomotion modes and transitions, relying solely on sEMG data. When forecasting 100 ms ahead, Deep-STF achieved an improved average prediction accuracy of 96.60%, outperforming seven benchmark models. Even with an extended 500ms prediction horizon, the accuracy only marginally decreased to 93.22%. The averaged stable prediction times for detecting next upcoming transitions spanned from 31.47 to 371.58 ms across the 100-500 ms time advances. Although the prediction accuracy of the trained Deep-STF initially dropped to 71.12% when tested on four new terrains, it achieved a satisfactory accuracy of 92.51% after fine-tuning with just 5 trials and further improved to 96.27% with 15 calibration trials. These results demonstrate the remarkable prediction ability and adaptability of Deep-STF, showing great potential for integration with walking-assistive devices and leading to smoother, more intuitive user interactions.

Improvement of visual servo system of industrial robot based on sliding mode control and deep reinforcement learning

Abstract. Visual servo system is more and more widely used in the field of industrial robots because it allows robots to sense external signals through sensors to convey control commands to themselves and complete tasks. However, the traditional visual servo has many limitations in the design of controller and image feature extraction, such as insufficient robustness and image extraction accuracy. This research focuses on the optimization of controller and image feature extraction, which can improve the overall performance and autonomy of the system by combining sliding mode control and Convolutional Neural Network (CNN). Sliding mode control performs well in terms of robustness and response speed, while CNN has excellent ability for image feature extraction. The research results show that the combination of the two and the visual servo has better performance in a variety of application scenarios, so this is also the development direction that industrial robots can adopt in the future.

HE-Nav: A High-Performance and Efficient Navigation System for Aerial-Ground Robots in Cluttered Environments

HE-Nav: A High-Performance and Efficient Navigation System for Aerial-Ground Robots in Cluttered Environments

LUO-V2 and MSPerception: A multi-fin co-drive robotic fish and its multi-sensor-based experimental system

Experimental systems for analyzing robotic fish swimming play a crucial role in optimizing robotic fish, which prompted us to design an experimental system that combines information from multiple sensors to address the shortcomings of current experimental systems with a single measurement. Furthermore, we are interested in a multi-fin co-drive robotic fish. This paper presents the design of a robotic fish, LUO-V2, and an experimental system, MSPerception. The LUO-V2 represents a novel solution for developing multi-fin co-drive robotic fish. It also provides a controller for LUO-V2, which can synchronously control the multi-fin of the robotic fish. Furthermore, the MSPerception system has been designed based on the fusion of multi-sensor information, which can sense the average thrust, swimming speed, and flow field, which provides an effective tool for studying robotic fish swimming. MSPerception was employed to investigate the factors influencing the swimming performance of LUO-V2 and to compare it with the flow field around the caudal fin of a real fish. The experimental results demonstrated that the multi-fin co-drive significantly enhanced the robotic fish’s performance. This research can provide a theorectical foundation for developing multi-fin co-drive robotic fish and experimental systems.

SMART SHOPPING ASSISTANTS: HOW GUIDE ROBOTS IMPROVE CUSTOMER EXPERIENCE IN LARGE MALLS

The Shopping Guide Robot System is a cutting-edge solution designed to enhance the customer experience in large shopping malls and commercial spaces. This system provides real-time route guidance to help customers quickly locate their desired stores by calculating the shortest path. It offers personalized recommendations based on customer profiles, adjusts dynamically to environmental changes, and supports multilingual communication to cater to diverse clientele. The robot not only guides users to their destinations but also answers inquiries about mall events, discounts, restaurants, and other services. Through advanced autonomous navigation technology, the system ensures a safe and efficient shopping experience, making it an invaluable tool in modern retail environments.

Cooperative Object Transport Via Non-Contact Prehensile Pushing by Magnetic Forces

Abstract Cooperative robot systems are an essential candidate for object transportation solutions. They offer cost-efficient and flexible operation for various types of robotic tasks. The benefits of cooperative robot systems have triggered the improvement of the object transportation field. In this study, a new way of transporting objects by cooperative robots is presented. The proposed method is performed by the pushing action of the magnetic forces of the robots. The permanent magnets mounted on the mobile robots and the cart create this repelling force. The rectangular object carrier cart equipped with passive caster wheels can be manipulated on flat terrains easily and be assigned to carry different shapes of objects. Using a carrier cart has the advantage of eliminating the vertical loads on the robots. Controlling a non-contact pushing method offers a low computational burden since simple velocity and position updates are adequate for operation management. Compared with the other methods of object transportation systems, the non-contact pushing method provides a faster operation with less sensitivity to control errors. Both simulations and real-world experiments are conducted and the performances are given comparatively with a generalized frictional contact object-pushing method. The results show that the proposed method provides 10.48% faster and 20.03% more accurate object transportation compared to the frictional contact method. It is envisioned that the presented method can be a promising candidate for object transportation tasks in the industry.

Proof-of-Concept Development of the Distal Module of a Cystoscope Transurethral Continuum Surgical Robotic System

Proof-of-Concept Development of the Distal Module of a Cystoscope Transurethral Continuum Surgical Robotic System

A Newly Custom Coordinate System Used for Preoperative Planning of Robotic-assisted Total Knee Arthroplasty

PurposePreoperative planning is critical for total knee arthroplasty (TKA) performed with surgical robots, as it involves establishing a coordinate system to calculate the angle values of the components. This coordinate system serves as a reference during the surgical planning stage. This study aimed to develop a newly custom coordinate system suitable for integration with a surgical robot system. MethodsThe "Skywalker" surgical robot system was used to import computed tomography (CT) images of the entire lower extremities from 50 patients diagnosed with osteoarthritis. Three-dimensional reconstruction was subsequently performed. The TKA component was positioned at a fixed angle using the newly developed custom coordinate system. The angle values of the components, based on the standard CT coordinate system, were then recorded without altering their positioning. These values were analyzed to assess the differences between the two coordinate systems. ResultsThe mean and standard deviation values for the coronal, sagittal, and transverse plane positioning of the femoral component (absolute value of error) were 0.004° ± 0.020°, 0.006° ± 0.024°, and 0.158° ± 0.186°, respectively. Similarly, the mean and standard deviation values for the coronal, sagittal, and transverse plane positioning of the tibial component (absolute value of error) were 0.544° ± 0.452°, 0.042° ± 0.076°, and 0.348° ± 0.445°, respectively. ConclusionThis newly developed custom coordinate system can be employed for preoperative planning in Skywalker surgical robot system-assisted TKA, particularly for patients with significant positional abnormalities in their CT scans.

Assessment of first-touch skills in robotic surgical training using hi-Sim and the hinotori surgical robot system among surgeons and novices

PurposeSurgeons’ adaptability to robotic manipulation remains underexplored. This study evaluated the participants’ first-touch robotic training skills using the hinotori surgical robot system and its simulator (hi-Sim) to assess adaptability.MethodsWe enrolled 11 robotic surgeons (RS), 13 laparoscopic surgeons (LS), and 15 novices (N). After tutorial and training, participants performed pegboard tasks, camera and clutch operations, energizing operations, and suture sponge tasks on hi-Sim. They also completed a suture ligation task using the hinotori surgical robot system on a suture simulator. Median scores and task completion times were compared.ResultsPegboard task scores were 95.0%, 92.0%, and 91.5% for the RS, LS, and N groups, respectively, with differences between the RS group and LS and N groups. Camera and clutch operation scores were 93.1%, 49.7%, and 89.1%, respectively, showing differences between the RS group and LS and N groups. Energizing operation scores were 90.9%, 85.2%, and 95.0%, respectively, with a significant difference between the LS and N groups. Suture sponge task scores were 90.6%, 43.1%, and 46.2%, respectively, with differences between the RS group and LS and N groups. For the suture ligation task, completion times were 368 s, 666 s, and 1095 s, respectively, indicating differences among groups. Suture scores were 12, 10, and 7 points, respectively, with differences between the RS and N groups.ConclusionFirst-touch simulator-based robotic skills were partially influenced by prior robotic surgical experience, while suturing skills were affected by overall surgical experience. Thus, robotic training programs should be tailored to individual adaptability.

The Practicality of STEAM Based Augmented Reality (AR) Learning Media for Vocational High School Students in Robotic System Subject

This study aims to create and implement STEAM-based Augmented Reality (AR) learning media for the class XI robotics systems curriculum at SMK N 1 West Sumatra. The research follows a Research and Development (R&D) approach using the ADDIE development model. Participants included class XI students and robotics systems teachers. Data collection methods comprised questionnaires and interviews, with descriptive data analysis employed to assess the validity and practicality of the developed learning media. The findings indicate that the STEAM-based AR learning media is effective. According to media expert validation, the average score was 0.88 (≥ 0.66), categorizing it as valid. Material expert validation yielded an average score of 0.85 (≥ 0.66), also marking it as valid. The practicality assessment showed that teacher responses averaged 90.14%, and student responses averaged 92.03%, both rated as very practical. Consequently, the STEAM-based AR learning media is considered suitable for class XI robotics systems at SMK N 1 West Sumatra, based on the validity and practicality tests

AI-Aided Robotic Wide-Range Water Quality Monitoring System

AI-Aided Robotic Wide-Range Water Quality Monitoring System

Three-Dimensional Fluoroscopic System to Assess Robotically Placed Pedicle Screws: Should We Confirm Robotic Pedicle Screw Placement With Advanced Imaging?

Retrospective cohort study. The purpose of this study is to determine the utility of advanced imaging to confirm the placement of robotic pedicle screws. With increasing robotic adoption, certain institutions and surgeons have developed protocols for obtaining 3D intraoperative imaging after robotic pedicle screw placement to ensure proper hardware placement. No studies have assessed the utility of these protocols relative to the potential risks of increased radiation exposure and operative time. The purpose of this study is to determine if we should be obtaining advanced imaging to confirm the placement of robotic pedicle screws. This is a single institution retrospective cohort study of patients from May 2022 to July 2023 who underwent lumbar spinal fusion by a high-volume orthopedic spine surgeon at a level 1 metropolitan hospital. All cases used combined robotics and navigation systems for pedicle screw placement and intraoperative 3D imaging for evaluation of screw position. Pedicle screw accuracy was assessed using the Gertzbein and Robbins system (GRS). Acceptable pedicle screw position was defined as GRS A or B. Seventy patients with 354 robotically placed pedicle screws were assessed with intraoperative 3D fluoroscopy. All pedicle screws were placed in either a GRS type A or type B position. Three hundred forty-seven were placed in a GRS A classification (99.2%, 351/354), and 3 were placed in a GRS B classification (0.08% 3/354). No patients had screw-related complications. The average radiation dosage of 3D imaging was 289.7±164.6mGy. The robotic system places pedicle screws accurately without 3D intraoperative imaging. Given the increased radiation and operative time associated with 3D imaging protocols 3D imaging scans should only be obtained in cases with heightened clinical concern. Level IV.

An empirical evaluation of a formal approach versus ad hoc implementations in robot behavior planning

As autonomous robotic systems integrate into various domains, ensuring their safe operation becomes increasingly crucial. A key challenge is guaranteeing safe decision making for cyber-physical systems, given the inherent complexity and uncertainty of real-world environments.Tools like Gwendolen, vGOAL, and Tumato enable the use of formal methods to provide guarantees for correct and safe decision making. This paper concerns Tumato, a formal planning framework that generates complete behavior from a declarative specification. Tumato ensures safety by avoiding unsafe actions and states while achieving robustness by considering nondeterministic outcomes of actions. While formal methods claim to manage complexity, provide safety guarantees, and ensure robustness, empirical evaluation is necessary to validate these claims.This work presents an empirical study comparing the characteristics of various ad hoc behavior planning implementations (developed by participants with diverse levels of experience in computer science), with implementations using Tumato. We investigate the usability of the different approaches and evaluate i) their effectiveness, ii) the achieved safety (guarantees), iii) their robustness in handling uncertainties, and iv) their adaptability, extensibility, and scalability. To our knowledge, this is the first participant-based empirical study of a formal approach for (safe and robust) autonomous behavior.Our analysis confirms that while ad hoc methods offer some development flexibility, they lack the rigorous safety guarantees provided by formal methods. The study supports the hypothesis that formal methods, as implemented in Tumato, are effective tools for developing safe autonomous systems, particularly in managing complexity and ensuring robust decision making and planning.

Comparative Analysis of Value Iteration and Policy Iteration in Robotic Decision-Making

This study focuses on Markov Decision Processes (MDPs) as a framework for decision-making in robotics. MDPs are crucial for enabling autonomous systems to operate in dynamic environments. MDPs enable the development of optimal strategies that balance immediate and future rewards, making them essential for intelligent robotic behavior. The purpose of this study is to evaluate the performance differences of value iteration and strategy iteration algorithms in different robot tasks. The study highlights the strengths and weaknesses of each algorithm. This comparative analysis uniquely addresses a gap in existing research by evaluating both Value Iteration and Policy Iteration side by side, offering critical insights into their respective performances across diverse robotic tasks. Results indicate that Policy Iteration converges faster and adapts better in complex environments, making it ideal for real-time applications, while Value Iteration is more efficient in smaller state spaces.These findings provide critical insights for selecting algorithms tailored to specific robotic applications, highlighting their role in advancing intelligent robotic systems.

A Robotic Teleoperation System with Integrated Augmented Reality and Digital Twin Technologies for Disassembling End-of-Life Batteries

Disassembly is a key step in remanufacturing, especially for end-of-life (EoL) products such as electric vehicle (EV) batteries, which are challenging to dismantle due to uncertainties in their condition and potential risks of fire, fumes, explosions, and electrical shock. To address these challenges, this paper presents a robotic teleoperation system that leverages augmented reality (AR) and digital twin (DT) technologies to enable a human operator to work away from the danger zone. By integrating AR and DTs, the system not only provides a real-time visual representation of the robot’s status but also enables remote control via gesture recognition. A bidirectional communication framework established within the system synchronises the virtual robot with its physical counterpart in an AR environment, which enhances the operator’s understanding of both the robot and task statuses. In the event of anomalies, the operator can interact with the virtual robot through intuitive gestures based on information displayed on the AR interface, thereby improving decision-making efficiency and operational safety. The application of this system is demonstrated through a case study involving the disassembly of a busbar from an EoL EV battery. Furthermore, the performance of the system in terms of task completion time and operator workload was evaluated and compared with that of AR-based control methods without informational cues and ‘smartpad’ controls. The findings indicate that the proposed system reduces operation time and enhances user experience, delivering its broad application potential in complex industrial settings.

Integrating subject-specific workspace constraint and performance-based control strategy in robot-assisted rehabilitation.

The robot-assistive technique has been widely developed in the field of neurorehabilitation for enhancement of neuroplasticity, muscle activity, and training positivity. To improve the reliability and feasibility in this patient-robot interactive context, motion constraint methods and adaptive assistance strategies have been developed to guarantee the movement safety and promote the training effectiveness based on the user's movement information. Unfortunately, few works focus on customizing quantitative and appropriate workspace for each subject in passive/active training mode, and how to provide the precise assistance by considering movement constraints to improve human active participation should be further delved as well. This study proposes an integrated framework for robot-assisted upper-limb training. A human kinematic upper-limb model is built to achieve a quantitative human-robot interactive workspace, and an iterative learning-based repulsive force field is developed to balance the compliant degrees of movement freedom and constraint. On this basis, a radial basis function neural network (RBFNN)-based control structure is further explored to obtain appropriate robotic assistance. The proposed strategy was preliminarily validated for bilateral upper-limb training with an end-effector-based robotic system. Experiments on healthy subjects are enrolled to validate the safety and feasibility of the proposed framework. The results show that the framework is capable of providing personalized movement workspace to guarantee safe and natural motion, and the RBFNN-based control structure can rapidly converge to the appropriate robotic assistance for individuals to efficiently complete various training tasks. The integrated framework has the potential to improve outcomes in personalized movement constraint and optimized robotic assistance. Future studies are necessary to involve clinical application with a larger sample size of patients.

Framework for Integrating Large Language Models with a Robotic Health Attendant for Adaptive Task Execution in Patient Care

The development of intelligent medical service robots for patient care presents significant challenges, particularly in integrating diverse knowledge sources and enabling robots to autonomously perform tasks in dynamic and unpredictable healthcare environments. This study introduces a novel framework that combines large language models with healthcare-specific knowledge and robotic operations to enhance autonomous task execution for a Robotic Health Attendant. Utilizing OpenAI’s ChatGPT, the system processes structured information about patient care protocols and unstructured human inputs to generate context-aware robot actions. A prototype system was tested in a simulated patient room where the robot successfully performed both simple individual actions and complex tasks involving the execution of multiple actions, based on real-time dialogues with the language model and predefined task specifications. The results demonstrate the potential of language models to reduce the reliance on hardcoded logic and provide healthcare professionals with the ability to interact with robotic systems through natural language.

Model-based design of a mechanically intelligent shape-morphing structure

Soft robotics has significant interest within the industrial applications due to its advantages in flexibility and adaptability. Nevertheless, its potential is challenged by low stiffness and limited deformability, particularly in large-scale application scenarios such as underwater and offshore engineering. The integration of smart materials and morphing structures presents a promising avenue for enhancing the capabilities of soft robotic systems, especially in large deformation and variations in stiffness. In this study, we propose a multiple smart materials based mechanically intelligent structure devised through a model-based design framework. Specifically, the intelligent structure incorporates smart hydrogel and shape memory polymer (SMP). Employing the finite element method (FEM), we simulated the complex interactions among smart material to analyze the performance characteristics of the intelligent structure. The results demonstrate that, utilizing smart hydrogel and shape memory polymer (SMP) can effectively attain large deformation and exhibit variable stiffness due to the shape memory effect. Besides, the shape-morphing structures exhibit customized behaviours including bending, curling, and elongation, all while reducing reliance on external power sources. In conclusion, utilizing multiple smart materials within the model-based design framework offers an efficient approach for developing mechanically intelligent structure capable of complex deformations and variable stiffness, thereby providing support for underwater or offshore engineering applications.

Clash of the Titans: the first multi-center retrospective comparative study between da Vinci and Hugo™ RAS surgical systems for the treatment of deep endometriosis

Background The proliferation of several robotic platforms presents an opportunity to pinpoint the most suitable system for specific procedures and patient profiles. This study aims to explore differences in complications and functional outcomes among patients undergoing deep endometriosis excision with the da Vinci surgical system compared to the Hugo™ RAS system. Method This is a retrospective, multicenter cohort study. Patients were categorized based on the surgical system used: the Da Vinci system and the Hugo™ RAS system. Perioperative complications, functional outcomes (via validated questionnaire: BFLUTS, KESS, GIQLI), and pain symptoms both before and after surgery were compared between the two groups. Results A total of six postoperative complications were reported: four in the Da Vinci system group (20%) and two in the Hugo™ RAS system group (12.5%). No difference in the mean operative time (p = 0.647), median estimated blood loss (p = 0.179), and hospital stay (p < 0.0001) was found between the two groups. A significant difference was reported in questionnaire score changes and dyspareunia severity in the da Vinci system arm. Conclusions Both robotic systems offer comparable performances in terms of intraoperative complications, although there was a higher incidence of postoperative complications in patients who underwent surgery with the Da Vinci system. Moreover, there was an improvement in dyspareunia, urinary, and gastrointestinal function in the same group.