Robot Experiments Research Articles

BN SO59 - Learning Curve for Robotic-Assisted Cholecystectomy

Abstract Background Assessment of learning curves following the introduction of robotic-assisted technology has shown that improvement in surgical outcomes can be seen with increasing robotic experience. Method All robotic-assisted cholecystectomies performed by a single consultant upper gastrointestinal surgeon as primary operator were analysed between 6/8/21 - 6/6/24 at a UK specialist centre using the Versius robotic system (CMR surgical). We recorded set up time during cases over this period. Results 38 robotic-assisted cholecystectomies were performed within the study period. In the first 10 cases, average set up time was 8 minutes and 18 seconds. In the second 10 cases this reduced to 5 minutes and 36 seconds. No further improvement was seen after case 20. Conclusion Our results demonstrate that set up time for robotic-assisted cholecystectomy improves over the first 20 cases. This data suggests 20 cases are adequate for acquiring the skillset required to set up the Versius robotic system efficiently in real life practice.

The learning curve for robotic-assisted pyeloplasty in urologists with no prior robotic experience using an <i>ex-vivo</i> model: A prospective, controlled study

Introduction: Despite the increasing trend of utilizing robotic techniques in pyeloplasty, little is known about the learning curve for robot-assisted pyeloplasty (RAP) amongst urologists with no prior robotic experience. Therefore, the present study aimed to evaluate the learning curve of residents in the last year or new urologists performing RAP using an ex-vivo model.Methods: A prospective ex-vivo model study was conducted including participants who were either residents in the last year or new urologists. All participants had obtained the E-BLUS certification or they were able to complete its 4 tasks successfully in a dry lab, without prior robotic experience. Each participant performed four consecutive RAPs using the Avatera system on an ex-vivo porcine model. The primary endpoint of the present study was the change in the average time to complete the anastomosis from the first to fourth attempts.Results: Nine urologists and 8 residents were enrolled in this study. All participants successfully completed the four RAP attempts. Each surgeon demonstrated a reduction in the time to complete anastomosis from the 1st to 4th attempt with an average of value of 4.41±1.06 minutes (p=0.003). The decrease in time was statistically significant in both urologists and residents subgroups (4.5±1.41 minutes p=0.049 and 4.33±0.71 minutes p=0.035, respectively). Conclusions: The training on the ex-vivo model could lead, in only a few attempts, to a significant improvement in skills and in the required time of experienced-naïve surgeons to complete a RAP.

Evaluating the User Experience and Usability of the MINI Robot for Elderly Adults with Mild Dementia and Mild Cognitive Impairment: Insights and Recommendations

Introduction: In recent years, the integration of robotic systems into various aspects of daily life has become increasingly common. As these technologies continue to advance, ensuring user-friendly interfaces and seamless interactions becomes more essential. For social robots to genuinely provide lasting value to humans, a favourable user experience (UX) emerges as an essential prerequisite. This article aimed to evaluate the usability of the MINI robot, highlighting its strengths and areas for improvement based on user feedback and performance. Materials and Methods: In a controlled lab setting, a mixed-method qualitative study was conducted with ten individuals aged 65 and above diagnosed with mild dementia (MD) and mild cognitive impairment (MCI). Participants engaged in individual MINI robot interaction sessions, completing cognitive tasks as per written instructions. Video and audio recordings documented interactions, while post-session System Usability Scale (SUS) questionnaires quantified usability perception. Ethical guidelines were followed, ensuring informed consent, and the data underwent qualitative and quantitative analyses, contributing insights into the MINI robot’s usability for this demographic. Results: The study addresses the ongoing challenges that tasks present, especially for MD individuals, emphasizing the importance of user support. Most tasks require both verbal and physical interactions, indicating that MD individuals face challenges when switching response methods within subtasks. These complexities originate from the selection and use of response methods, including difficulties with voice recognition, tablet touch, and tactile sensors. These challenges persist across tasks, with individuals with MD struggling to comprehend task instructions and provide correct answers and individuals with MCI struggling to use response devices, often due to the limitations of the robot’s speech recognition. Technical shortcomings have been identified. The results of the SUS indicate positive perceptions, although there are lower ratings for instructor assistance and pre-use learning. The average SUS score of 68.3 places device usability in the “good” category. Conclusions: Our study examines the usability of the MINI robot, revealing strengths in quick learning, simple system and operation, and integration of features, while also highlighting areas for improvement. Careful design and modifications are essential for meaningful engagement with people with dementia. The robot could better benefit people with MD and MCI if clear, detailed instructions and instructor assistance were available.

Application and optimisation of intelligent control system for home robots

Abstract. As a significant part of the smart home, intelligent home robots can provide a variety of convenient home services such as automatic cleaning, elderly care, entertainment interaction, etc., which are highly valued and favoured by the majority of home users, and the market scale is expanding. However, the design and optimization of the control system represents a crucial technical challenge to truly realize the intelligence and autonomy of home robots. This paper presents a comprehensive analysis of the design and optimization of automatic control systems for intelligent home robots, which aims to improve the performance and user experience of intelligent home robots and better meet the needs of families. Through collection and analysis of the research on domestic robots and their control technology ,the core technical elements of the control system are summarized and refined, and the hardware structure and software framework of the control system are analyzed. The research results provide valuable insights for improving the performance and capabilities of home robot control systems to better meet the diverse needs and expectations of home users. These innovations provide a solid foundation for advancing the development of intelligent robots in the home. Future trends in control systems may include enhanced perceptual capabilities and decision-making intelligence to enable robots to better understand and meet the needs of home users.

Making surgical education intuitive: A surgical robotics primer for pre-clinical medical students

BackgroundAs robotic surgeries increase nationwide, residency programs are implementing commensurate curriculum. Medical student exposure and comfort with these surgeries, however, is lagging. This program sought to improve student interest and confidence through additional robotic exposure. MethodsA two-part educational program was implemented at an academic institution. Part-one included a surgeon-led lecture and part-two a hands-on robotics primer where students were exposed to 3-D anatomy and instrumentation via robotic console. Data was collected via RedCap and analyzed for significance (p ​< ​0.05). ResultsThirty-two students participated in part one, ten of which were selected for part two. The majority (82 ​%) reported being interested or very interested in pursuing additional robotic experiences and 40 ​% reported improved confidence in actively assisting in a robotics case (p ​< ​0.005). ConclusionConducting robotic exposure events improves medical students' confidence and interest in seeking future robotic surgery experiences. As robotic surgery expands, medical students have shown to benefit from earlier exposure.

Rethinking the four-wing problem in plesiosaur swimming using bio-inspired decentralized control

A locomotor system that can function across different environmental conditions and produce a range of performances is one of the most critical abilities needed for extant and extinct animals in order to survive and maximise their competitive fitness. Recent engineering-inspired paleontological studies have reconstructed feasible locomotor patterns in extinct animals. However, it is still challenging to describe how extinct animals successfully adjust their locomotor patterns to new situations (e.g., changes in locomotor speed and morphology). In this study, we develop a novel reconstruction method based on a bio-inspired control system. We focus on plesiosaurs, an extinct aquatic reptile group which has two pairs of flipper-shaped limbs, and demonstrate that a highly optimised, flexible locomotor pattern for all four flippers can be reconstructed based on a decentralized control scheme formulated from extant animals’ locomotion. The results of our robotic experiments show that a simple, local sensory feedback mechanism allows the plesiosaur-like robot to exploit the fluid flow between the flippers and generate efficient swimming patterns in response to changes in locomotor conditions. Our new method provides further evidence how decentralized control systems can produce a pathway between extinct and extant animals in order to understand the how extinct animals moved and how these movement patterns may have evolved.

GRID-FAST: A Grid-based Intersection Detection for Fast Semantic Topometric Mapping

This article introduces a novel approach to constructing a topometric map that allows for efficient navigation and decision-making in mobile robotics applications. The method generates the topometric map from a 2D grid-based map. The topometric map segments areas of the input map into different structural-semantic classes: intersections, pathways, dead ends, and pathways leading to unexplored areas. This method is grounded in a new technique for intersection detection that identifies the area and the openings of intersections in a semantically meaningful way. The framework introduces two levels of pre-filtering with minimal computational cost to eliminate small openings and objects from the map which are unimportant in the context of high-level map segmentation and decision making. The topological map generated by GRID-FAST enables fast navigation in large-scale environments, and the structural semantics can aid in mission planning, autonomous exploration, and human-to-robot cooperation. The efficacy of the proposed method is demonstrated through validation on real maps gathered from robotic experiments: 1) a structured indoor environment, 2) an unstructured cave-like subterranean environment, and 3) a large-scale outdoor environment, which comprises pathways, buildings, and scattered objects. Additionally, the proposed framework has been compared with state-of-the-art topological mapping solutions and is able to produce a topometric and topological map with up to 92% fewer nodes than the next best solution. The method proposed in this article has been implemented in the robotics framework ROS and is open-sourced. The code is available at: https://github.com/LTU-RAI/GRID-FAST.

Research on the influence of cutter overhang length on robotic milling chatter stability

Serial industrial robots are widely in demand in the field of large-scale component processing and manufacturing. However, the structure characteristic of serial robots makes it easy to generate chatter during milling and the overhang length of milling cutter has an important influence on the stability of robotic milling. Milling cutter overhang length refers to the length from the tip of the cutter to the protrusion of the spindle, to study the influence of milling cutter overhang length on the stability behavior of robotic milling, the modal parameters of milling cutter under different overhang length were obtained by hammer experiment, and its dynamic characteristics were analyzed. The stability behavior for robotic milling under different cutter overhang lengths was analyzed by using the stability lobe diagrams, and the stability lobe diagrams were verified by robotic milling experiments. The results show that the rigidity and natural frequency of milling cutter decrease with the increase of overhang length. There is a big gap between the stability lobe diagrams and the actual machining state when the cutter overhang length is short, and the low frequency vibration is easy to occur in the robotic milling process when the spindle speed is low, which is most probably because that the absorbed vibration energy by the cutter has been transferred to the robot body before the milling cutter vibrates. When the cutter overhang is increased and the feed direction is along the y-axis of the robot global coordinate system, the stability lobe diagrams by considering the multi-mode coupling effect is more consistent with the actual milling state.

A concurrent learning approach to monocular vision range regulation of leader/follower systems

This paper explores range and bearing angle regulation of a leader–follower using monocular vision. The main challenge is that monocular vision does not directly provide a range measurement. The contribution is a novel concurrent learning (CL) approach, called CL Subtended Angle and Bearing Estimator for Relative pose (CL-SABER), which achieves range regulation without communication, persistency of excitation or known geometry and is demonstrated on a physical, robot platform. A history stack estimates target size which augments the Kalman filter (KF) with a range pseudomeasurement. The target is followed to scale without drift, persistency of excitation requirements, prior knowledge, or additional measurements. Finite excitation is required to achieve parameter convergence and perform steady-state regulation using CL-SABER. Evaluation using simulation and mobile robot experiments in special Euclidean planar space (SE(2)) show that the new method provides stable and consistent range regulation, as demonstrated by the inter-rater reliability, including in noisy and high leader acceleration environments.

Finite-time optimal control for a class of nonlinear systems with performance constraints via critic-only ADP: Theory and experiments

This paper addresses the optimal control problem within the framework of adaptive dynamic programming (ADP) for a class of nonlinear systems subjected to performance constraints. A new finite-time optimal control scheme is developed to stabilize the system by using the critic-only neural network ADP method. Compared with the existing ADP-based optimal control methods with uniformly ultimately bounded stability, the provided control scheme ensures that the controlled system's state and neural network weight estimation error are finite-time stable. It can ensure optimality, prescribed performance, and finite-time stability of the closed-loop control system simultaneously through an integration of ADP, the prescribed performance control technique, and Lyapunov theory. The designed adaptive neural network weight update law can relax the persisting excitation condition. The proposed control scheme is implemented on a robotic experiment platform to achieve trajectory tracking and verify its effectiveness.

Adaptive performance optimal control for flexible-joint robots with random noises: Design and experiment

This study developes a flexible performance optimal control scheme via reinforcement learning strategy and event-triggered mechanism for flexible-joint robots with random noise and non-affine input. It is notable that an event-triggered optimization mechanism is developed, which meets the optimality principle and saves communication resources. Nevertheless, the existing event-triggered strategy is unable to handle non-affine input, which limits the applicability of this method. To overcome the above problems, a modified event-triggered mechanism is proposed. At the same time, the optimal solution of the system is given by an optimized control algorithm based on the improved performance index function. In the controller design, neural network is used to deal with random disturbances and uncertainties, and an adaptive law is designed to replace the identifier structure. Besides, a flexible prescribed performance function is constructed to yield multiple prescribed performance behaviors by adjusting the key parameters, while the tracking error is stayed within a prescribed boundary. Finally, the effectiveness of the proposed control scheme is further demonstrated by simulation and the experiment of the 2-link flexible-joint robot on the Quanser platform.

Differentiable modeling and optimization of non-aqueous Li-based battery electrolyte solutions using geometric deep learning

Electrolytes play a critical role in designing next-generation battery systems, by allowing efficient ion transfer, preventing charge transfer, and stabilizing electrode-electrolyte interfaces. In this work, we develop a differentiable geometric deep learning (GDL) model for chemical mixtures, DiffMix, which is applied in guiding robotic experimentation and optimization towards fast-charging battery electrolytes. In particular, we extend mixture thermodynamic and transport laws by creating GDL-learnable physical coefficients. We evaluate our model with mixture thermodynamics and ion transport properties, where we show improved prediction accuracy and model robustness of DiffMix than its purely data-driven variants. Furthermore, with a robotic experimentation setup, Clio, we improve ionic conductivity of electrolytes by over 18.8% within 10 experimental steps, via differentiable optimization built on DiffMix gradients. By combining GDL, mixture physics laws, and robotic experimentation, DiffMix expands the predictive modeling methods for chemical mixtures and enables efficient optimization in large chemical spaces.

Analysis of Initial Outcomes of Gas-insufflation One-step Single-port Transaxillary (GOSTA) Robotic Thyroidectomy for Thyroid Cancer.

This study compared the initial outcomes of gas-insufflation one-step single-port transaxillary (GOSTA) robotic thyroidectomy with traditional open thyroidectomy for thyroid cancer at a single institution. A retrospective analysis was conducted on 77 patients who underwent thyroidectomy for differentiated thyroid cancer from January to June 2024. Exclusion criteria included benign nodules, Graves' disease, and previous thyroid surgeries. Two surgeons performed the procedures, with one having no prior robotic surgery experience. Of the 77 patients, 48 underwent open thyroidectomy and 29 underwent GOSTA thyroidectomy. The GOSTA group had a significantly lower mean age (40.1 vs. 47.6 years, p=0.002) and a higher proportion of female patients (p=0.040). The open group patients had more harvested lymph nodes than the GOSTA group patients (7.9 vs. 2.4, p<0.001). The GOSTA group patients had longer operation time (156.4 vs. 80.6 min, p<0.001), and had extended hospital stay than the open group patients (5.9 vs. 3.4 days, p<0.001). Complication rates were similar between the groups. GOSTA robotic thyroidectomy provides comparable safety and effectiveness to open thyroidectomy, with improved cosmetic outcomes despite longer operation times and hospital stays. This technique is feasible for surgeons without prior robotic experience, offering a viable alternative for patients prioritizing cosmetic results.

Hydrodynamic Simulation and Experiment of a Self-Adaptive Amphibious Robot Driven by Tracks and Bionic Fins.

Amphibious robots have broad prospects in the fields of industry, defense, and transportation. To improve the propulsion performance and reduce operation complexity, a novel bionic amphibious robot, namely AmphiFinbot-II, is presented in this paper. The swimming and walking components adopt a compound drive mechanism, enabling simultaneous control for the rotation of the track and the wave-like motion of the undulating fin. The robot employs different propulsion methods but utilizes the same operation strategy, eliminating the need for mode switching. The structure and the locomotion principle are introduced. The performance of the robot in different motion patterns was analyzed via computational fluid dynamics simulation. The simulation results verified the feasibility of the wave-like swimming mechanism. Physical experiments were conducted for both land and underwater motion, and the results were consistent with the simulation regulation. Both the underwater linear and angular velocity were proportional to the undulating frequency. The robot's maximum linear speed and steering speed on land were 2.26 m/s (2.79 BL/s) and 442°/s, respectively, while the maximum speeds underwater were 0.54 m/s (0.67 BL/s) and 84°/s, respectively. The research findings indicate that the robot possesses outstanding amphibious motion capabilities and a simplistic yet unified control approach, thereby validating the feasibility of the robot's design scheme, and offering a novel concept for the development of high-performance and self-contained amphibious robots.

A Gecko‐Inspired Robot Using Novel Variable‐Stiffness Adhesive Paw Can Climb on Rough/Smooth Surfaces in Microgravity

Space‐wall‐climbing robots face the challenge of stably attaching to and moving on spacecraft surfaces, which include smooth flat areas and rough intricate surfaces. Although adhesion‐based wall‐climbing robots demonstrate stable climbing on smooth surfaces in outer space, there is scarce research on their stable adhesion on rough surfaces within a microgravity environment. A novel adhesive material is developed inspired by the adhesion mechanism and locomotion of the Gekko gecko. This material exhibits exceptional adhesion across various materials and surface roughness. A variable‐stiffness gecko‐inspired paw is engineered, generating substantial adhesion forces while minimizing detachment forces. Impressively, this paw generates up to 180 N of adhesion force on smooth surfaces and achieves detachment without external forces. By integrating such variable‐stiffness paws with a wall‐climbing robot, a gecko‐inspired robot effectively operating in a microgravity environment is created. The robotic satellite surface climbing experiments and robotic satellite capture experiments are conducted using a simulated microgravity environment and a satellite model. The results unequivocally demonstrate the gecko‐inspired robot's proficiency in executing various functions, including stable motion and capture on both smooth and rough spacecraft surfaces within a microgravity environment. These experiments underscore the potential of adhesion‐based gecko‐inspired robots for in‐orbit services and spacecraft capture and recovery.

Initial Experience of Robot Assisted Laparoscopic Pyeloplasty for Ureteropelvic Junction Obstruction Using the Hinotori Surgical Robot System.

This study aimed to investigate the perioperative outcomes of robot-assisted laparoscopic pyeloplasty (RLP) using the recently launched hinotori surgical robot system. This retrospective study compared the perioperative outcomes of 11 consecutive patients who underwent RLP with the hinotori surgical robot system from October 2022 to March 2024 (hinotori group) and 30 consecutive patients who underwent RLP with the da Vinci system from March 2019 to September 2022 (da Vinci group). The patient characteristics of the groups were similar. The median operative times in the hinotori and da Vinci groups were 236.0 and 231.5min, respectively (p=0.480). The success rates were 100.0% and 96.7%, respectively (p=1.000). Clavien-Dindo grade ≥3 complications occurred in one patient (9.1%) in the hinotori group and one patient (3.3%) in the da Vinci group (p=0.470). The perioperative outcomes in the hinotori group were not inferior to those in the da Vinci group.

Application of Artificial Neuromolecular System in Robotic Arm Control to Assist Progressive Rehabilitation for Upper Extremity Stroke Patients

Freedom of movement of the hands is the most desired hope of stroke patients. However, stroke recovery is a long, long road for many patients. If artificial intelligence can assist human arm movement, the possibility of stroke patients returning to normal hand movement might be significantly increased. This study uses the artificial neuromolecular system (ANM system) developed in our laboratory as the core of motion control, in an attempt to learn to control the mechanical arm to produce actions similar to human rehabilitation training and the transition between different activities. This research adopts two methods. The first is hypothetical exploration, the so-called “artificial world” simulation method. The detailed approach uses the V-REP (Virtual Robot Experimentation Platform) to conduct different experimental runs to capture relevant data. Our policy is to establish an action database systematically to a certain extent. From these data, we use the ANM system with self-organization and learning capabilities to develop the relationship between these actions and establish the possibility of conversion between different activities. The second method of this study is to use the data from a hospital in Toronto, Canada. Our experimental results show that the ANM system can continuously learn for problem-solving. In addition, our three experimental results of adaptive learning, transfer learning, and cross-task learning further confirm that the ANM system can use previously learned systems to complete the delivered tasks through autonomous learning (instead of learning from scratch).

Beyond the Learning Curve of Robot-Assisted Navigation Spine Surgery: Refinement of Outcomes With Extended Experience.

Objective This study assessed whether robotic-assisted navigation (RAN) spine surgery outcomes, including operative time and pedicle screw accuracy, continue to improve with extended experience beyond 200 cases. Methods This is a retrospective review of 60 patients who underwent lumbosacral transforaminal interbody fusion using RAN. Patients were segmented into three groups of 20 consecutive cases each. The first group represented a surgical performance baseline leading up to the investigating surgeon's 200th RAN case. The subsequent two groups were selected beyond the 200th case with an average of 15 cases between groups. Pedicle screw accuracy and intraoperative outcomes were assessed. Statistical results were significant if p<0.05. Results Measures of surgical efficiency significantly improved beyond the investigating surgeon's 200th RAN case. As case number increased, the following parameters significantly decreased: registration time (group 1: 16.9±6.5, group 2: 12.9±3.0, group 3: 8.7±1.6 minutes; p<0.05), screw insertion time (group 1: 14.9±3.5, group 2: 10.9±2.0, group 3: 8.4±2.7 minutes; p<0.05), and total operative time significantly decreased from group 1 (175.9±58.2 minutes) to group 2 (135.8±23.9 minutes) (p=0.013) with a non-significant decrease to group 3 (121.5±32.3 minutes). Accuracy (Grade = A) significantly increased across groups (group 1: 87%, group 2: 94%, group 3: 98%; p=0.024). Group 1 had the highest misplacement rate of 3.7% (4/108 screws). The overall misplacement rate was 1.4% (4/290 screws) (Grade C-E). There was a higher rate of lateral screw misplacement compared to medial misplacement. Conclusion Even with a small number of initial cases, RAN spine surgery can consistently be performed with high accuracy and acceptable intraoperative outcomes. However, this study demonstrated refined outcomes with extended robotic experience.

Development of a Novel Retinal Surgery Robot Based on Spatial Variable Remote Center-of-Motion Mechanism

Abstract This article reports the design, modeling, and experiments of a novel retinal surgery robot based on spatial variable remote center-of-motion (RCM) mechanism. The general design criteria for parallel mechanisms are proposed, and the planar five-bar mechanisms are evaluated and selected. The planar-spatial evolution process, including the parallel connection of the planar mechanism and the equivalent substitution of joints, is adopted to develop a spatial variable RCM mechanism and then the robot. The mobility and singularity of the robot are analyzed, and the forward/inverse kinematics and workspace are modeled. Dimension optimization is conducted based on a comprehensive performance indicator that characterizes the motion range of linear actuators and the global dexterity performance index of robot. The prototyped robot is fabricated and assembled, and the kinematic calibration is performed. The position error of end-effector is within 34 μm, and both the position error and deviation of the RCM point are within 23 μm. The robot is demonstrated to reach the desired position and execute the RCM motion with high precision simultaneously.

Wing-strain-based flight control of flapping-wing drones through reinforcement learning

Although drone technology has advanced rapidly, replicating the dynamic control and wind-sensing abilities of biological flight is still beyond reach. Biological studies reveal that insect wings are equipped with mechanoreceptors known as campaniform sensilla, which detect complex aerodynamic loads critical for flight agility. By leveraging robotic experiments designed to mimic these biological systems, we confirm that wing strain provides crucial information about the drone’s attitude angle, as well as the direction and velocity of the wind. We introduce a wing-strain-based flight controller that employs the aerodynamic forces exerted on a flapping drone’s wings to deduce vital flight data such as attitude and airflow without accelerometers and gyroscopic sensors. The present work spans five key experiments: initial validation of the wing strain sensor system for state information provision, control in a single degree of freedom movement environment with changing winds, control in a two degrees of freedom movement environment for gravitational attitude adjustment, a test for position control in windy conditions and a demonstration of precise flight path manipulation in a windless condition using only wing strain sensors. We have successfully demonstrated control of a flapping drone in various environments using only wing strain sensors, with the aid of a reinforcement-learning-driven flight controller. The demonstrated adaptability to environmental shifts will be beneficial across varied applications, from gust resistance to wind-assisted flight for autonomous flying robots.