Robot Experiments Research Articles

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.

Design, Analysis and Experiment of a Modular Deployable Continuum Robot

Continuum robots, possessing great flexibility, can accomplish tasks in complex work scenes, regarded as an important direction in robotics. However, the current continuum robots are not satisfying enough in terms of fabrication and maintenance, and their workspace is limited by structure and other aspects. In this paper, to address the above problems, a modular deployable robot, which adopts an origami structure instead of a flexible hinge, is proposed. A fabrication method is innovated, the Spherical Linkage Parallel Mechanism (SLPM) unit is optimized, and the installation and fabrication process of the robot is simplified through modularization. The forward kinematics and inverse kinematics of the robot and its workspace are analyzed by using the screw theory. The prototype of the robot is constructed, and its folding performance, bending performance, and motion accuracy are tested, and the error analysis and compensation optimization are carried out. After the optimization, the position error of the robot is reduced by about 65%, and the standard deviation is greatly lowered, which effectively improves the motion accuracy and stability of the robot.

Analysis of structural parameters of stirred engineered composite robots in high-risk environments

Facing the safety of firefighting operators in high-risk environments, this paper investigates a robot for mixing engineering composites, which can automatically monitor the homogeneity of the mixing and, together with an automatic material feeding system, can realize automated production. This Study is based on the numerical simulation method that explored the discrete element model of engineering composites, carried out the multifactorial structural parameter orthogonal simulation experiment of the mixing robot in EDEM software, investigated the effects of different structural parameters on mixing time, mixing uniformity and productivity, proposed a mixing robot concrete uniformity intelligent identification algorithm.

Low-frequency chatter suppression for robotic milling using a novel MRF absorber

Robotic milling has a unique advantage for large and complex parts. However, it is extremely prone to low-frequency chatter due to the robot structure mode. In robotic milling, low-frequency chatter has a huge impact on machining quality and efficiency. In this paper, a novel MRF (Magnetorheological fluid) absorber is used to suppress low-frequency chatter during robotic milling based on the proposed low-frequency chatter suppression strategy. First, the GPR (Gaussian process regression) model is used to predict the impulse response function based on a few measured impulse response functions. Next, a cutting force model is developed considering the tool runout and the robot structure mode. The parameters and coefficients of the cutting force model are optimized by the measured cutting forces. Then, the low-frequency chatter frequency is predicted considering the modal coupling effect based on the predicted impulse response function and the simulated cutting force. Finally, an MRF absorber is designed based on the chatter frequency range. The controlled storage modulus of the MRF in the pre-yield region enables a wider frequency shift characteristic of the designed MRF absorber. The MRF absorber controls the input current based on the predicted low-frequency chatter frequency to achieve chatter suppression during robotic milling. Robotic milling experiments verified that the MRF absorber can effectively suppress low-frequency chatter. The experimental results show that the Energy Ratio Chatter Indicator of the industrial robot with MRF absorber is less than 0.1 under different milling conditions.

A Terminal Residual Vibration Suppression Method of a Robot Based on Joint Trajectory Optimization

Vibration problems have become one of the most important factors affecting robot performance. To this end, a terminal residual vibration suppression method based on joint trajectory optimization is proposed to improve the accuracy and stability of robot motion. Firstly, based on the characteristics of the friction nonlinearity due to joint coupling and physical feasibility of dynamic parameters, a semidefinite programming method is used to identify dynamic parameters with actual physical meaning, thereby obtaining an accurate dynamic model. Then, based on the result of the residual vibration time domain analysis, a joint trajectory optimization model with the goal of minimizing joint tracking error is established. The Chebyshev collocation method is used to discretize the optimization model. The dynamic model is used as the optimization constraint, and barycentric interpolation is used to obtain the optimized joint motion trajectory. Finally, industrial robot experiments prove that the vibration suppression method proposed in this article can reduce the maximum acceleration amplitude of residual vibration by 62% and the vibration duration by 71%. Compared with the input shaping method, the method proposed in this paper can reduce the terminal residual vibration more effectively and ensure the consistency of running time and trajectory.

Skill progress during a dedicated societal robotic surgery training curriculum including several robotic surgery platforms.

Robot-assisted procedures are increasingly common, and several systems are available for thoraco-abdominal surgery. Specific structured training is necessary, while access to these systems is still limited. This study aimed to assess surgeons' skill progress during consecutive training days of a curriculum with exposure to different robotic systems. This prospective observational study enrolled 47 surgeons with anonymized analysis of SimNow™ simulator performance scores and dedicated questionnaires after written consent. The primary outcome was the overall score, based on economy of motion, time to complete the exercise, and penalty for errors. Course participants in 2022-2023 had chosen 2 full hands-on days on Da Vinci® consoles with either virtual reality (VR) simulation training using the SimNow (n = 21, 44.7%) or digestive surgery procedures with a live animal model (n = 26, 55.3%). In all participants, training on Da Vinci® systems included console functions and principles of docking, camera, and instrument use for console and procedural training. They additionally had access to introductory dry-lab and VR simulator exercises on the Versius, HugoTMRAS, and Dexter systems and to VR exercises on the ROBOTiS simulator. The participants (16F/31M, median age 40years, range 29-58) from various surgical specialties (general/visceral/vascular) had no (n = 35, 74.5%) or little (n = 12, 25.5%) robotic experience including bedside assistance only and 20 (42.6%) had robotic simulator experience. The demographic variables fully completed by 44/47 participants (93.6%) and choice of module had no significant impact on the primary outcome. The considerable performance improvement from days 1 to 2 was exemplified by a significantly increased economy of motion and decreased amount of excessive force. Robotic surgical training is increasingly complex with several systems on the market. Within a dedicated robotic surgery curriculum and based on integrated performance metrics, a significant improvement of skill levels was observed in a relatively short period of time.

End-to-end deep reinforcement learning and control with multimodal perception for planetary robotic dual peg-in-hole assembly

The planetary construction is necessary for long-term scientific deep space exploration and resource utilization in the future. The planetary robotic assembly control is a key technology that must be broken through in future planetary surface construction. The paper focuses on the most representative dual peg-in–hole assembly, which has sufficiently complex contact interaction, wide range of applications and good method portability. To address the challenges brought by the unstructured planetary environment and the features of the construction tasks, the paper proposes an end-to-end deep reinforcement learning and control method with multimodal perception for planetary robotic assembly tasks. A staged reward function based on the visual virtual target point for policy learning is designed. The effectiveness and feasibility of the proposed control method have been verified through simulation experiments and ground real robot experiments. It provides a feasible control method of robotic operations for future planetary surface construction.

CLIP feature-based randomized control using images and text for multiple tasks and robots

This study presents a control framework leveraging vision language models (VLMs) for multiple tasks and robots. Notably, existing control methods using VLMs have achieved high performance in various tasks and robots in the training environment. However, these methods incur high costs for learning control policies for tasks and robots other than those in the training environment. Considering the application of industrial and household robots, learning in novel environments where robots are introduced is challenging. To address this issue, we propose a control framework that does not require learning control policies. Our framework combines the vision-language CLIP model with a randomized control. CLIP computes the similarity between images and texts by embedding them in the feature space. This study employs CLIP to compute the similarity between camera images and text representing the target state. In our method, the robot is controlled by a randomized controller that simultaneously explores and increases the similarity gradients. Moreover, we fine-tune the CLIP to improve the performance of the proposed method. Consequently, we confirm the effectiveness of our approach through a multitask simulation and a real robot experiment using a two-wheeled robot and robot arm.

Public perception of socially assistive robots for healthcare in the EU: A large-scale survey

This paper presents the results of a large-scale survey (n = 1092) that explored the attitudes and opinions of European Citizens regarding the adoption of socially assistive robots (SARs) for healthcare in the EU. We examined which functions citizens would support and which they consider a threat to trustworthy SARs. We additionally explored the relationships between the perceived vulnerability of the care recipient and acceptance, between attitudes towards robots and gender, age, religious beliefs, and previous experience interacting with SARs, and whether the degree of responsibility taken in performing a role affects acceptance. We also compared attitudes towards robots across European regions. The functions most negatively rated were triage and banning entrance. Privacy raised particular concern. We also found an inverse correlation between the perceived vulnerability of care recipients and acceptance. Additionally, we found a positive relationship between religious beliefs and fear of robots, a positive relationship between previous robot experience and attitudes towards them, and that females have less positive attitudes towards robots than males. Also, the degree of responsibility in a role determined acceptance. Involving citizens in the decisions concerning SARs deployment is important to build a society that people feel is fair in terms of robot coexistence. The results of the survey intend to provide evidence-based support to policies in this area.

Recruiting older participants: evaluating the role of message framing in willingness to enroll in wearable robot experiments

Recruiting older participants: evaluating the role of message framing in willingness to enroll in wearable robot experiments

Implementation of the Versius robotic surgical system for thoracic surgery: first clinical evaluation of feasibility and performance.

The aim of this study is to demonstrate the ability of the Versius surgical system to successfully and safely complete a range of thoracic procedures aligned with Stage 2a (Development) of the Idea, Development, Exploration, Assessment and Long-term follow-up framework for surgical innovation. This prospective study included the first 30 consecutive patients who underwent robotic surgery with Versius by 2 surgeons without prior robotic experience between 1 April 2023 and 30 December 2023 [25 lung resections (wedge, segmentectomy and lobectomy) and 5 thymectomies]. There were no specific predetermined selection criteria for each case. The primary outcome was safe completion of the procedure without unplanned conversion. Secondary outcomes included intraoperative and postoperative complications, intraoperative device-related outcomes and pathology results. Twenty-eight (93.3%) cases were completed without conversion. Both conversions were to thoracoscopy, one due to a 'console alarm' and the other due to pulmonary artery bleeding. In lung resections, median console time was 103 (90-129) min. Five (20%) patients experienced postoperative complications, most frequent was persistent air leak (16%). Median length-of-stay was 3 (2-4) days. Neither readmissions nor mortality was observed. In thymectomies, no intraoperative or postoperative complications, readmissions, reinterventions or mortality were observed. Median console time was 77 (75-89) min and median length of stay was 1 (1-1) day. This phase 2a IDEAL-D study confirms lung resections and thymectomies are feasible with the use of Versius system, laying the foundation for larger phase 2b and 3 clinical studies within the IDEAL-D framework.

MOB-Net: Limb-modularized uncertainty torque learning of humanoids for sensorless external torque estimation

Momentum observer (MOB) can estimate external joint torque without requiring additional sensors, such as force/torque or joint torque sensors. However, the estimation performance of MOB deteriorates due to the model uncertainty which encompasses the modeling errors and the joint friction. Moreover, the estimation error is significant when MOB is applied to high-dimensional floating-base humanoids, which prevents the estimated external joint torque from being used for force control or collision detection in the real humanoid robot. In this paper, the pure external joint torque estimation method named MOB-Net, is proposed for humanoids. MOB-Net learns the model uncertainty torque and calibrates the estimated signal of MOB, substantially reducing the estimation errors of MOB. The external joint torque can be estimated in the generalized coordinate including whole-body and virtual joints of the floating-base robot with only internal sensors (an IMU on the pelvis and encoders in the joints). Furthermore, MOB-Net shows more robust performance for the unseen data compared to the end-to-end learning method, and the robustness of MOB-Net is validated through extensive simulations, real robot experiments, and ablation studies. Finally, various collision handling scenarios are presented to show the versatility of MOB-Net: contact wrench feedback control for locomotion, collision detection, and collision reaction for safety.

Kinematic of 3-Wheels Swerve Drive Using BLDC Motor

The stability of the robot's performance is very important, especially for the wheeled mobile robots that use swerve drives, which need kinematic control to reach the destination point. The study of robot movement known as kinematics is based on an examination of the geometric structure of the robot, with no consideration given to the mass, force, or acceleration that the robot experiences during movement. This study aims to model and simulate the kinematic control design of a wheeled robot that uses a swerve drive. This robot uses BLDC motor actuator so that the robot can reach its destination very quickly and steadily. The test is carried out by simulating and comparing the performance response using BLDC motors and DC motors. According to the testing and trials, the robot can reach its destination by modeling its kinematic control, and BLDC motors are found to be more reliable and efficient for driving and steering than DC motors.

Global POD Modes From PIV Asynchronous Patches

A method is proposed to obtain full-domain spatial modes based on Proper Orthogonal Decomposition (POD) of Particle Image Velocimetry (PIV) measurements performed at different (overlapping) spatial locations. When performing robotic volumetric Particle Image Velocimetry (PIV) (Jux et al., 2018), the large-scale mean velocity fields are estimated merging several measurements performed at different (adjacent) locations covered by the robot sequence. The proposed methodology leverages the definition of POD modes as eigenvectors of the spatial correlation matrix to obtain also large-scale modes. Performing measurements over overlapping (50-75%) regions allows to approximate the correlation matrix in the two adjacent domains. When applied over a sequence of views, this method has the potential to deliver full-domain POD modes spanning the volume covered by the robot sequence, even if different regions are covered asynchronously. This methodology is particularly well-suited for applications that seek to investigate large-scale flow structures, whenever the dynamic spatial range (DSR) of the measurement system does not allow to capture the whole domain at once. The methodology is validated using a 2D experimental dataset of a turbulent boundary layer, where patches are artificially created from splitting the PIV measurements, later used as ground truth to assess the results. Furthermore, we apply the technique to a 3D robotic volumetric PIV experiment of the flow around a wall-mounted cube.

Light Reflections Detection And Correction For Robotic Volumetric PIV

Laser light reflection mitigation in Particle Image Velocimetry (PIV) is crucial for accurate flow field measurements. While numerous methods exist for planar PIV, fewer have been developed for volumetric PIV systems, and in particular for coaxial setups like robotic volumetric PIV. Light reflections in volumetric PIV experiments result in high-intensity regions that corrupt particle detection and analysis. This study presents a novel approach for treating light reflections in robotic volumetric PIV experiments. The proposed method uses image filtering and masking techniques in the wavenumber space to separate particle images from reflection regions. The process involves decomposing the image signal into low- and high-wavenumber components using the 2D discrete Fourier transform (DFT) to then use a high-pass filter to attenuate the intensity of the reflection regions. Finally, a step of automated adaptive masking is applied to remove residual reflection areas that the filtering is not able to eliminate. The proposed approach is tested on experimental data obtained from experiments performed using robotic volumetric PIV on two different geometries: a side-view mirror and a rotating two-blade propeller. Comparison between raw and pre-processed images, as well as particle tracking results, is presented. The results from this data comparison show successful removal of reflection-induced artifacts in instantaneous images by using the spatial Fourier filter automated masking approach. The developed image pre-processing strategy effectively removes unsteady light reflection regions in robotic volumetric PIV images, preventing the appearance of spurious particle tracks and improving the accuracy of flow field measurements. The spatial gaps introduced by the masking procedure can be easily filled in via measurements from multiple directions, which are promptly achieved via the robotic volumetric PIV approach.

772 Assessing the Impact of Gaming Skills on Robotic Surgical Skill Acquisition and Learning Curves

Abstract Robotic surgery has rapidly advanced across various medical specialties over the past decade. Extensive research has confirmed the benefits of robotic surgery, leading to improved surgical outcomes and patient satisfaction. Historically, only highly experienced consultants in laparoscopic and open surgery received training in robotic surgery to the detriment of junior surgical trainees. Virtual reality simulators have introduced gamification to enhance surgical skills. Studies have shown that individuals with gaming experience, musical aptitude, and sports backgrounds exhibit improved psychomotor skills relevant to surgery, including hand-eye coordination, reaction time, and visuospatial awareness. Despite this, there is a lack of research on how gaming and musical abilities impact robotic surgical skill acquisition and learning curves. This prospective, observational, and comparative study involved 15 novice surgeons with no prior robotic experience recruited from various UK medical institutions. Participants were divided into two groups based on their video game experience. While statistical significance was not reached (p 0.26), an observed trend favoured the gaming cohort's performance (M 56.29, SD 57.85) on the robotic surgical simulator compared to the non-gamer group (M 38.41, SD 46.77) possibly due to the limited sample size. Gamers exhibited notably superior reaction time scores (p 0.037) Additionally, gamers demonstrated enhanced overall performance in spatial reasoning and the Beat Saber game, assessing hand-eye coordination, though these differences did not reach statistical significance. This study suggests that video gaming experience may provide novice surgeons with certain advantages when beginning training on the high-fidelity dVSS robotic surgical simulator. Further research with larger participant groups may confirm these findings.

A self-adaptive agent for flexible posture planning in robotic milling system

In recent years, industrial robots are widely used in the milling processes of the fundamental components such as aeronautic parts, aerospace cabins and blades on the marine propellers of the worships, because of their flexible structure, large operating space, and robust capacity of rapid reconfiguration. Along with the robotic machining process, multiple types of geometrical and physical constraints display the time-varying characteristics, which are difficult to be considered simultaneously by the traditional static planning methods on the robotic milling posture. Therefore, a deep reinforcement learning (DRL) enhanced virtual repulsive potential field flexible model is proposed for the robotic milling posture dynamic planning. The preliminary planning of static milling posture is realized based on the fixed setting of the virtual modal parameters in the posture planning model, which is treated as a theory preparation for training the deep reinforcement learning agent. To develop the static planning model, multiple constraints are established by considering the performances of geometric, kinematic and machining precision simultaneously. Furthermore, a reward function is constructed for the dynamic agent, comprehensively combining the long and short reward benefits in nonlinear correlation with virtual repulsive forces. By taking the virtual modal parameters as the action space of the dynamic agent, a DRL enhanced agent VRPF-SAC is created and trained under the dynamic milling environment from virtual repulsive planning field. Four tasks of large size robotic milling simulations and experiments are designed and developed to validate the effectiveness of the flexible planning model. In comparison with the static model, the flexible planning model displays the excellent global effects covering the kinematic, and precision performance, with 65.78 %, 72.04 %, and 70.25 % reduction in velocity, acceleration, and jerk of robotic joints, and ensuring the effective precision performance from −0.365 to 0.283 mm of large size workpiece with large curvature changes. The proposed flexible posture planning architecture can provide a basic for robotic dynamic milling of core parts with large size, narrow space and sharp curvatures of the aeronautic parts, aerospace cabins and worship propellers.

Dual flexible contact material removal model for robotic disk grinding

High-precision machining of flexible materials, such as rubber, presents a challenge in manual grinding. The implementation of robotic grinding has the potential to significantly enhance machining precision and efficiency. Normally, an accurate material removal model is needed for precise planning of the robotic grinding parameters. Existing models are mostly built for metal materials, and neglect the large deformation of flexible workpieces, resulting in deficiencies in accuracy and generalization. To this end, a nonlinear dual flexible contact force model is presented, considering the significant deformation of both the tool and the workpiece, to characterize the dual flexible contact mechanism. Then, the contact force model is combined with the Preston equation, yielding the material removal model. The effectiveness and superiority of our contact force model and material removal model are substantiated by force–displacement experiments and robotic grinding experiments, respectively. These experiments show a remarkable reduction of 68.17% in the root-mean-square error (RMSE) for the contact force model and a noteworthy 29.31% decrease in RMSE for the material removal model.

Feedback neural computation in collision perception: Towards diverse selectivity

Significant strides have been achieved in the biological exploration of the locust’s lobula giant movement detector (LGMD), contributing substantially to the development of collision detection vision systems. Two LGMD neurons, namely LGMD1 and LGMD2, have undergone extensive modeling, each exhibiting distinct collision selectivity toward brighter or darker objects approaching relative to the background. Despite these advancements, a gap exists between biological organisms and the latest models, particularly in implementing diverse collision selectivity and maintaining robustness against objects with varying brightness. To reduce the gap, we introduce a neural model with feedback connections that integrates a feedforward neural network based on ON/OFF channels and feedback loops of ON channels operating merely ON-contrast signals. The instantaneous feedback leads to a fixed-point theorem, establishing the coefficient range in the feedback loop mathematically. This research emphasizes and theoretically analyzes the influence of feedback neural computation on collision perception. To validate the model’s effectiveness, we defined an evaluation criterion, i.e., the time error to collision (TTC), and compared the proposed model with the latest LGMD1 and LGMD2 models. Systematic experiments demonstrated that the proposed model achieves specific collision selectivity comparable to both LGMD1 and LGMD2 while exhibiting enhanced robustness against objects with changing brightness on the surface, outperforming the comparative models. The proposed model predicts collision danger more accurately and robustly, yielding lower TTC. Finally, we carried out on-line robot experiments, which proved the practicality and efficiency of the proposed feedback neural computation in embedded vision system.