Robot Arm Research Articles

Suitability of UR5 Robot for Robotic 3D Printing

The present paper describes the measurement of the drift of unidirectional pose accuracy, repeatability, and static compliance of a collaborative robot employing a measurement methodology that relies on the description of a virtual ISO cube placed in the robot’s workspace. The measurements aimed to investigate and assess the suitability of the UR5 six-axis collaborative robot for its application in robotic 3D printing. An experimental laboratory measurement workstation was constructed to perform the measurements, and control measurements were performed. The measurements involved describing the TCP point of the robot tool at five measurement points located in a virtual ISO cube during a minimum of 30 repeated measurement cycles. A camera and six linear incremental sensors with assessment units were used for the measurements. The measurements were performed in compliance with the regulations of STN ISO 9283 standard for this type of measurement. As a result of the measurements, the technical specifications of the drift and static compliance of the controlled robotic arm were verified, and the results were compared with the values specified by the manufacturer. Following the measurements and assessment of the results, it was possible to assess the suitability of the used UR5 robotic arm for its application in robotic 3D printing and to propose possible recommendations for the calibration of the robot and the process settings of the printing system for the production of objects using FDM technology.

Flexible Continuum Robot with Variable Stiffness, Shape‐Aware, and Self‐Heating Capabilities

Conventional continuum robots have outstanding flexibility and dexterity. However, when the robot needs to interact with the environment, the softness may affect the performance of the robot. Especially in transport tasks, the softness of continuum robots can lead to handling failures and drastic drops in precision. The variable stiffness continuum robot combines the advantages of flexibility and rigidity, which is conducive to expanding the application scenarios of flexible continuum robots. This article proposes a flexible continuum robot that simultaneously realizes variable stiffness, shape‐aware, and self‐heating functions using liquid metal. The low‐temperature phase transition property of liquid metal is utilized to realize the variable stiffness function; the overall stiffness of the robot can reach the range of 18.5–183 N m−1, which can realize a tenfold stiffness gain. The conductivity of liquid metal is utilized to develop the shape‐aware function, and the monitoring accuracy is within 5%. At the same time, this article utilizes the liquid metal's resistive thermal effect to realize heating function, so that the robot no longer needs heating systems such as heating wires and can realize the phase transition by energizing itself. Based on this design, the robot arm can realize the transition between maximum and minimum stiffness within 240 s.

Robots in the kitchen: The automation of food preparation in restaurants and the compounding effects of perceived love and disgust on consumer evaluations

Restaurants are swiftly embracing automation to prepare food, experimenting with innovations from robotic arms for frying foods to pizza-making robots. While these advances promise to enhance efficiency and productivity, their impact on consumer psychology remains largely unexplored. We present four experiments that demonstrate how food service automation leads to negative downstream effects (i.e., diminished taste perceptions, decreased willingness to pay, less favorable attitudes towards food items) across multiple food categories. This stems in part from two distinct contagion effects, whereby automation appears to undermine the food's ability to contain symbolic love (positive contagion from human contact) while simultaneously increasing feelings of disgust (negative contagion from machine contact). Moreover, we highlight how communicating the consumer-oriented benefits of automation can suppress the disgust associated with automation and subsequently mitigate the deleterious effects on consumer evaluations. Our findings suggest that service retailers should consider the psychological impact on consumers when shifting away from human involvement in a category as intimate and consequential as the production of our food.

Feasibility of novel robot-assisted, remotely-performed echocardiographic examination

Abstract Background Access to echocardiography services outside metropolitan areas is persistently limited. We sought to determine the feasibility and efficiency of performing remotely-controlled, semi-automated robot-assisted echocardiographic examinations (robot-assisted echo) by cardiac sonographers as a potential avenue to provide standard echocardiographic specialist services to remote and rural areas as well as permit expedited diagnoses enabling earlier management of disease. Methods Two accredited cardiac sonographers (Cardiac Investigations Unit) undertook 6 hours virtual training, and 10 hours hands-on robot-assisted echo simulation training; using a novel, semi-automated robotic arm system to remotely manipulate an ultrasound probe to acquire echocardiographic images. Sonographers then performed remotely-performed robot-assisted echo examinations on staff volunteers, and consenting inpatients in whom echocardiography was clinically indicated. Cameras, probe pressure sensors and an ultrasound platform remote console were utilised to support remote operation. Traditional echocardiography (manipulation of probe by hand) was also performed as a reference. Feasibility to obtain a comprehensive robot-assisted echo was compared to traditional echocardiography. Sonographer learning curve and efficiency (image acquisition time and offline measurement time) was also investigated. Results Total cohort was n=78 (age 51±15yrs; 57% male) with 63% patients. All participants had robot-assisted echo and traditional echocardiograms performed within 72hrs. Average feasibility (%) of robot-assisted echo as (a) 11 averaged assessments and (b) 38 averaged measures was 92±9 (a) and 86±11 (b), compared to (a) 99±1 and (b) 97±7 by traditional examination (all respectively). Sonographer learning curve was identified via 1:1 block subcohort analysis, with averaged assessment feasibility (n=33:31) being 86±19 (first block) to 98±3 (last block) and averaged measurement feasibility (n=32:31) being 77±11 (first block) to 92±7 (last block), all respectively. In the staff participant cohort (n=31), average image acquisition efficiency in robot-assisted echo was poorer than that of traditional examination (47+9 minutes compared to 19+2 minutes; p<0.0001), respectively. Average efficiency of offline measurement time was 13+7 minutes by robot-assisted echo; comparable (p=0.33) to traditional examination at 12+6 minutes. There was no learning curve effect on image acquisition efficiency; 42+10 vs 51+7 minutes (1:1; n=16:15; p=0.007). Conclusion Remotely-performed, robot-assisted echo using a commercially-available ultrasound platform performed by briefly-trained, cardiac sonographers is feasible, with reasonable efficiency even though less efficient in image acquisition compared to traditional echocardiograms. Further investigation of performance as a remote service is warranted in larger, clinically-indicated populations.

Initial experience with a 5G cellular network and robotic arm-based remote echocardiographic system in an all-comers setting

Abstract Background Echocardiography is the test-of-choice for the initial evaluation of many cardiac diseases and requires expertise that is often limited in rural area. Robotic arm-assisted remote echocardigram has been attampted for teleconsultation but was limited to heart failure patients(ref), primarily due to the network delay in telecommunication and the subsequent incoordination in robotic arm control. Purpose We aim to assess the feasibility and diagnostic accuracy of a 5G cellular network and robotic arm-based remote echocardiographic system in an all-comers setting. Methods 51 pts were enrolled from the outpatient cardiology clinic of our institute. All pts underwent standard comprehensive echocardiography on a 5G cellular network and robotic arm-based remote echocardiographic system (the robotic part was deployed at our hospital and the control part was located at another campus 20 kilometers away) (Figure 1)and a conventional echocardiographic platform (at our hospital) successively. The order in which patients were examined on the remote and conventional instruments was randomly determined. There was no interval between the two examinations, and examinations of the same patient were performed by experienced but different cardiologists, who were blinded to each other’s diagnosis. The doctor who used the remote system was also randomly allocated and had not been previously specifically trained. The exams were real-time and diagnoses were made right after the exams. Results Of the 51 pts, the image quality was sufficient for diagnosis in 50 patients (48% female, 40±18 years). 1 pt was excluded because some key views could not be obtained using the remote system (98% of exams successful). 17 pts had positive findings, including 10 with a primary diagnosis of valvulopathy (1 Barlow's syndrome, 1 bicuspid aortic valve and 8 less-than-moderate regurgitation), 2 cardiac surgery follow-ups (1 case of aortic valve replacement and septal myectomy, and 1 case of mitral valve replacement and tricuspid annuloplasty), and 2 hypertrophic cardiomyopathy (including 1 case of obstruction at papillary muscle level), 2 with abnormal left ventricular wall motion(including 1 case of apical mural thrombus) (Figure 2), and 1 with congenital heart disease (secumdum atrial septal defect). The main diagnosis was consistent in 98% of cases (papillary muscle level obstruction was missed in one case), using conventional echocardiographic findings as the standard. Time for image acquision using remote echocardiography was significantly longer than conventional one (24 minutes 36 seconds ± 4 minutes 52 seconds vs. 16 minutes 15 seconds ± 1 minute 54 seconds, p<0.0001, paired t-test). Conclusions Comprehensive echocardiographic exam with a 5G cellular network and robotic arm-based remote system is feasible with relatively good diagnostic accuracy.Figure 1Figure 2

Toward a digital twin of a robot workcell: standards and methods

Digital twins are poised to improve the engineering, operation, and management of manufacturing facilities. However, implementing digital twins has been a challenge primarily due to a lack of resources and the absence of standardized digital twin frameworks and methods. This paper describes creating a digital twin of a robot workcell using available standards, tools, and methods. The workcell comprises collaborative robot arms, a computer numeric control (CNC) machine tool, and a coordinate measuring machine (CMM). The ISO 23247 standard provides a guideline for building the digital twin. Data are collected from physical devices and the MTConnect standard provides a format for communicating the data between the physical equipment and their digital counterparts. The machine components in the workcell are represented as models in the virtual world. This research shows that the above standards and methods support building a digital twin of a robot workcell. The data collected and communicated can be used to improve workcell functions such as quality management and predictive maintenance.

A flexible proximity-pressure–temperature tri-mode robotic sensor with stimulus discriminability, high sensitivity and wide perception range

Along with the explosive utilization of intelligent and bionic robotics, the rise of somatosensory system with excellent flexibility and multiple biological sensing characteristic emerges as a substantial crux of this domain. Herein, we propose a flexible high-performance multi-mode sensor for real-time proximity–pressure–temperature perception based on a monolithic sensing unit with fingerprint-like hierarchical architecture. The monolithic sensing unit, primarily constituted by a double-permeable ionic liquids/Multi-walled nanotubes conductive network, demonstrates dual-functionality in detecting pressure and temperature. Making use of the further synergy of rational topographical architecture engineering and feasible decoupling algorithm construction, extraordinary progress in sensing performances for both pressure and temperature are attained with negligible mutual interferences. Additionally, the sensor is capable of switching to touchless mode to detect objects at distance up to 200 mm, validating its remarkable proximity sensing ability. The multifunctional nature of sensor is further substantiated through its integration with a robotic hand, highlighting its practical applicability in advanced robotic systems.

Quality control of digestive tract reconstruction after robotic gastrectomy

With features such as high-definition magnification of the surgical field, filtering of hand tremor, and robotic arm with multi-degree-of-freedom rotatable wrist, surgical robot has unique advantages in various aspects of digestive tract reconstruction (DTR) after robotic gastrectomy (RG). Currently, there is still controversy about the selection and standardized application of DTR after RG for gastric cancer. To standardize the selection and implementation of DTR, we describe various aspects of quality control before DTR, selection of indications, principles of DTR, and prevention and management of postoperative complications of DTR. We also comment on the operation details of robotic-assisted and total robotic DTR, including manual suture, linear anastomosis with linear stapler, and circular anastomosis with circular stapler. Meanwhile, we propose our prospects on the potential application of new technology in robotic DTR for gastric cancer.

Research on Obstacle-Avoidance Trajectory Planning for Drill and Anchor Materials Handling by a Mechanical Arm on a Coal Mine Drilling and Anchoring Robot.

At present, China's coal mine permanent tunneling support commonly uses mechanized drilling and anchoring equipment; there are low support efficiency, labor intensity, and other issues. In order to further improve the support efficiency and liberate productivity, this paper further researches the trajectory planning of the drilling and anchoring materials of the robotic arm for the drilling machine "grasping-carrying-loading-unloading" on the basis of the drilling and anchoring robotic system designed by the team in the previous stage. Firstly, the kinematic model of the robotic arm with material was established by improving the D-H parameter method. Then, the working space of the robotic arm with the material was analyzed using the Monte Carlo method. The singular bit-shaped region of the robotic arm was restricted, and obstacles were removed from the working space. The inverse kinematics was utilized to solve the feasible domain of the robotic arm with material. Secondly, in order to avoid blind searching, the guidance of the Bi-RRT algorithm was improved by adding the target guidance factor, and the two-way tree connection strategy for determining the feasible domain was combined with the Bi-RRT algorithm's feasible domain judgment bi-directional tree connection strategy to improve the convergence speed of the Bi-RRT algorithm. Then, in order to adapt to the dynamic environment and avoid the global planning algorithm from falling into the local minima, on the basis of the above planning methods, an improved Bi-RRT trajectory planning algorithm incorporating the artificial potential field was proposed, which takes the planned paths as the guiding potential field of the artificial potential field to make full use of the global information and avoid falling into the local minimization. Finally, a simulation environment was built in a ROS environment to compare and analyze the planning effect of different algorithms. The simulation results showed that the improved Bi-RRT trajectory planning algorithm incorporating the artificial potential field improved the optimization speed by 69.8% and shortened the trajectory length by 46.6% compared with the traditional RRT algorithm.

Adaptive neuro-fuzzy inference system based active force control with iterative learning for trajectory tracking of a biped robot

ABSTRACT We investigate the integration of the active force control (AFC) scheme and the adaptive neuro-fuzzy inference system (ANFIS) as an intelligent controller algorithm to address trajectory-tracking problems in robotic systems, The AFC-ANFIS model exploits iterative learning (IL) to improve the tracking performance based on its trained model. The ANFIS parameters are tuned using both particle swarm optimisation (PSO) and beetle antennae search (BAS) algorithms. The simulation results of two different robots, i.e. a five-link biped robot and a PUMA 560 robot arm, indicate that the proposed AFC-ANFIS controller performs well for trajectory tracking and disturbances rejection. The AFC-ANFIS performance is evaluated and compared with those from other controllers using the average tracking error (ATE) metric. The comparative results reveal that AFC-ANFIS offers a viable approach with a rapid training process to undertaking trajectory tracking and disturbance rejection tasks.

Evaluation of a novel robotic testing method for stability and kinematics of total knee arthroplasty.

This work developed a novel preclinical test of total knee replacements (TKRs) in order to explain TKR instability linked to patient dissatisfaction. It was hypothesized that stability tests on the isolated moving prostheses would provide novel comparative data on the stability and kinematics among TKR designs. Three TKR designs, DePuy Synthes Attune MS, Stryker Triathlon and Zimmer Biomet Persona MC, were assessed using a robotic arm while flexing-extending 0-140°. Tests imposed 710 N body weight combined with three tibial loads: no anterior-posterior (AP) force, 90 N anterior or 90 N posterior force. Other load effects were minimized and the kinematics was recorded. Each implant was tested six times to investigate the repeatability of the method. Data were analysed using statistical parametric mapping with one-way analysis of variance (ANOVA). If significance was found (p < 0.05), post hoc t tests with Bonferroni correction were used to contrast groups. Significant differences were found throughout flexion-extension. Femoral rollback, AP stability, coupled internal-external rotation and AP position (roll-back) were all influenced by implant design. AP stability of the TKRs reduced with flexion reaching Attune 15 mm, Persona 13 mm and Triathlon 21 mm at 140° flexion. Tractive rolling significantly affected kinematics in the less congruent Triathlon design, with 6 mm different paths between flexion and extension motion (p < 0.05 across 5-100°). Paradoxical anterior femoral sliding in early flexion (0-40°) occurred in Persona and Triathlon designs. The novel testing technique provides, for the first time, comparative data on the inherent stability and kinematics of the TKR implants themselves across the arc of flexion-extension, independent of variables including soft tissue behaviour and surgical technique. The data show how much each prosthesis can contribute to the stability and motion of the implanted knee. Similar data from a wider range of designs will enable more informed decisions regarding implant design choice, aiming to reduce the prevalence of TKR instability in patients. Controlled laboratory study.

A robotic fish processing line enhanced by machine learning

This paper presents the design of a comprehensive automatic fish processing line utilizing machine learning algorithms. The processing line encompasses several essential steps, including fish identification by type, fish sorting by size, fish orientation based on shape, and fish cutting at the optimal chopping points. The primary objective of this design is not just automation but also maximizing economic benefits by preserving the maximum amount of fish meat during the cutting process, achieved through the application of machine learning algorithms. To accomplish these goals, we employ a combination of transfer learning and convolutional neural networks to establish criteria for actions across all stages of automatic fish processing. At the heart of the processing station is a conveyor belt equipped with numerous sensors and lenses. Positioned along this conveyor belt are two robotic arms, responsible for precise positioning and cutting operations, all guided by the machine learning algorithms. To provide a visual representation of these design concepts, we have created a 3D SolidWorks model.

Dexterous Manipulation Based on Object Recognition and Accurate Pose Estimation Using RGB-D Data.

This study presents an integrated system for object recognition, six-degrees-of-freedom pose estimation, and dexterous manipulation using a JACO robotic arm with an Intel RealSense D435 camera. This system is designed to automate the manipulation of industrial valves by capturing point clouds (PCs) from multiple perspectives to improve the accuracy of pose estimation. The object recognition module includes scene segmentation, geometric primitives recognition, model recognition, and a color-based clustering and integration approach enhanced by a dynamic cluster merging algorithm. Pose estimation is achieved using the random sample consensus algorithm, which predicts position and orientation. The system was tested within a 60° field of view, which extended in all directions in front of the object. The experimental results show that the system performs reliably within acceptable error thresholds for both position and orientation when the objects are within a ±15° range of the camera's direct view. However, errors increased with more extreme object orientations and distances, particularly when estimating the orientation of ball valves. A zone-based dexterous manipulation strategy was developed to overcome these challenges, where the system adjusts the camera position for optimal conditions. This approach mitigates larger errors in difficult scenarios, enhancing overall system reliability. The key contributions of this research include a novel method for improving object recognition and pose estimation, a technique for increasing the accuracy of pose estimation, and the development of a robot motion model for dexterous manipulation in industrial settings.

Biodegradable, stretchable, and high-performance triboelectric nanogenerators through interfacial polarization in bilayer structure

The increasing demand for wearable electronics has led to the development of triboelectric nanogenerators (TENGs) as a promising energy harvesting and sensing technology. However, conventional TENGs often utilize non-biodegradable materials, contributing to environmental pollution. In this work, we present a stretchable and biodegradable TENG based on hydroxyethyl cellulose (HEC) and gelatin (HG-TENG). The HG-TENG features a bilayered structure, where the large difference in their relative permittivity between HEC and gelatin induces interfacial polarization, effectively mitigating charge recombination and enhancing triboelectric performance. The optimized HG-TENG achieves an open-circuit voltage (Voc) of 93 V, a maximum power density of 57.8 µW/cm2, and can power 38 blue light-emitting diodes. The device exhibits a stretchability of 150 % and biodegrades within 3 hours in phosphate-buffered saline. Furthermore, we demonstrate the application of the HG-TENG as a wearable sensor by modifying it with trichloro(1H, 1H, 2H, 2H-perfluorooctyl)silane (FOTS) (FHG-TENG). The FHG-TENG-based smart glove, integrated with machine learning algorithms, enables real-time monitoring of blood pressure waveforms and finger motions, showcasing its potential for human-machine interfaces. The smart glove, equipped with five FHG-TENGs on the proximal interphalangeal joints of each finger, detects diverse finger gestures and generates voltage signals that control a robotic hand in real-time, demonstrating effective human-machine interaction through synchronized motion. Moreover, the smart glove achieves a high recognition accuracy of 96.15 % for 10 different hand sign languages.

PICASSO: a universal brain phantom for positron emission tomography based on the activity painting technique

Objective. This study presents a universal phantom for positron emission tomography (PET) that allows arbitrary static and dynamic activity distributions of various complexities to be generated using a single PET acquisition.Approach. We collected a high-statistics dataset (with a total of 22.4 × 109prompt coincidences and an event density of 2.75 × 106events mm-3) by raster-scanning a single plane with a22Na point source mounted on a robotic arm in the field-of-view of the uEXPLORER PET/CT scanner. The source position was determined from the reconstructed dynamic frames. Uniquely, true coincidences were separated from scattered and random events based on the distance between their line-of-response and the known source location. Finally, we randomly sampled the dataset to generate the desired activity distributions modeling several different phantoms.Main results. Overall, the target and the reconstructed phantom images had good agreement. The analysis of a simple geometric distribution showed high quantitative accuracy of the phantom, with mean error of <-3.0% relative to the ground truth for activity concentrations ranging from 5.3 to 47.7 kBq ml-1. The model of a high-resolution18F-fluorodeoxyglucose distribution in the brain illustrates the usefulness of the technique in simulating realistic static neuroimaging studies. A dynamic18F-florbetaben study was modeled based on the time-activity curves of a human study and a segmented brain phantom with no coincidences repeating between frames. For all time points, the mean voxel-wise errors ranged from -4.4% to -0.7% in grey matter and from -3.9% to +2.8% in white matter.Significance. The proposed phantom technique is highly flexible and allows modeling of static and dynamic brain PET studies with high quantitative accuracy. It overcomes several key limitations of the existing phantoms and has many promising applications for the purposes of image reconstruction, data correction methods, and system performance evaluation, particularly for new high-performance dedicated brain PET scanners.

Towards Transparent Control Systems: The Role of Explainable AI in Iterative Learning Control

This paper presents a novel approach to improving the performance and interpretability of Iterative Learning Control (ILC) systems through the integration of Explainable Artificial Intelligence (XAI) techniques. ILC is a powerful method used across various domains, including robotics, process control, and traffic management, where it iteratively refines control inputs based on past performance to minimize errors in system output. However, traditional ILC methods often operate as "black boxes," making it difficult for users to understand the decision-making process. To address this challenge, we incorporate XAI, specifically SHapley Additive exPlanations (SHAP), into the ILC framework to provide transparent and interpretable insights into the algorithm's behavior. The study begins by detailing the evolution of ILC, highlighting key advancements such as predictive optimal control and adaptive schemes, and then transitions into the methodology for integrating XAI into ILC. The integrated system was evaluated through extensive simulations, focusing on robotic arm trajectory tracking and traffic flow management scenarios. Results indicate that the XAI-enhanced ILC not only achieved rapid convergence and high control accuracy but also maintained robustness in the face of external disturbances. SHAP analyses revealed that parameters such as the proportional gain (Kp) and derivative gain (Kd) were critical in driving system performance, with detailed visualizations providing actionable insights for system refinement. A crucial metric for control precision was the root mean square error (RMSE), which was reduced to as low as 0.02 radians in the robotic arm case, indicating extremely precise tracking of the intended route. Similarly, the ILC algorithm effectively maintained the ideal traffic density within the predetermined bounds in the traffic management scenario, resulting in a 40% reduction in congestion compared to baseline control measures. The resilience of the ILC algorithm was also examined by introducing changes to the system model, external disturbances, and sensor noise. The algorithm demonstrated a high degree of stability and accuracy in the face of these disruptions. For instance, in the robotic arm case, adding noise to the sensor readings had a negligible effect on the algorithm's performance, increasing the RMSE by less than 5%. This integration of XAI into ILC addresses a significant gap in control system design by offering both high performance and transparency, particularly in safety critical applications. The findings suggest that future research could further enhance this approach by exploring additional XAI techniques and applying the integrated system to more complex, real-world scenarios.

High-Resolution Infrared Reflectance Distribution Measurement Under Variable Temperature Conditions.

The bidirectional reflectance distribution function (BRDF) can effectively characterize the reflectance properties of a target, which can be used to correct infrared remote sensing data and improve the accuracy of remote sensing measurements. When the surface temperature changes, the reflectance characteristics of the target usually change, and it is necessary to carry out BRDF measurements under variable temperature conditions. In this paper, a variable-temperature infrared BRDF measurement system based on a robotic arm is developed to realize high-resolution wide-temperature region measurement of BRDF. To improve the measurement accuracy, the shaping optical path was used to expand the laser beam, combined with the laser level to accurately adjust the three-dimensional coordinates of the robotic arm, and the dichotomy method is used to calibrate the detector nonlinearly. A portable heater suitable for the mechanical arm corner mechanism is developed, and fast and high-precision temperature control is realized by proportional integral derivative (PID) control. The specular and diffuse BRDF distributions were measured at room temperature to verify the effectiveness of the system. The BRDF distribution of SUS314 stainless steel samples with different roughness is measured during two temperature increases from 20 °C to 1000 °C, and the changing rule of BRDF under variable temperature environment is summarized, which provides technical support for evaluating the optical properties of high-temperature materials and improving the measurement accuracy of remote sensing data.

Accuracy of robotic-assisted surgery for immediate implant placement in posterior teeth: a retrospective case series

BackgroundRobotic computer-assisted implant surgery (r-CAIS) is a revolutionary innovation in oral implantation; however, the clinical feasibility of r-CAIS for immediate implant placement (IIP) in posterior teeth has not been verified. Thus, this study aimed to evaluate the accuracy of r-CAIS for IIP in posterior tooth regions.MethodsPatients with posterior teeth to be extracted and indicated to undergo r-CAIS were evaluated. The patients had positioning markers installed in the oral cavity and underwent cone-beam computed tomography (CBCT). Subsequently, minimally invasive tooth extractions were performed, and an individualised surgical plan was generated in the robotic software. After marker registration, implantation surgery was performed by the robotic arm under the supervision and assistance of the surgeons. Finally, the deviations between the planned and placed implants were evaluated based on preoperative and postoperative CBCT data.ResultsA total of 12 patients were evaluated. No adverse events occurred during the surgery. The mean global coronal, global apical, and angular deviations were 0.46 ± 0.15 mm (95%CI:0.36 to 0.56 mm), 0.46 ± 0.14 mm (95%CI:0.37 to 0.54 mm), and 1.05 ± 0.55° (0.69 to 1.40°), respectively.ConclusionsUnder the limited conditions of this study, the r-CAIS exhibited high accuracy in posterior teeth IIP surgery. Further multicentre randomised controlled studies are required to confirm the feasibility of this technology.

Vision-Based Localization Method for Picking Points in Tea-Harvesting Robots.

To address the issue of accurately recognizing and locating picking points for tea-picking robots in unstructured environments, a visual positioning method based on RGB-D information fusion is proposed. First, an improved T-YOLOv8n model is proposed, which improves detection and segmentation performance across multi-scale scenes through network architecture and loss function optimizations. In the far-view test set, the detection accuracy of tea buds reached 80.8%; for the near-view test set, the mAP0.5 values for tea stem detection in bounding boxes and masks reached 93.6% and 93.7%, respectively, showing improvements of 9.1% and 14.1% over the baseline model. Secondly, a layered visual servoing strategy for near and far views was designed, integrating the RealSense depth sensor with robotic arm cooperation. This strategy identifies the region of interest (ROI) of the tea bud in the far view and fuses the stem mask information with depth data to calculate the three-dimensional coordinates of the picking point. The experiments show that this method achieved a picking point localization success rate of 86.4%, with a mean depth measurement error of 1.43 mm. The proposed method improves the accuracy of picking point recognition and reduces depth information fluctuations, providing technical support for the intelligent and rapid picking of premium tea.

Grasp with push policy for multi-finger dexterity hand based on deep reinforcement learning

When interacting with the external environment, dexterous hands face various challenges, including interference in cluttered environments and difficulty in accurately locating target objects. We observe that the human hand is often at ease in the face of complex unstructured environments: pushing objects that can rearrange clutter, locating target objects, and creating space for fingers. In addition, the gripping action complements the push by achieving the precise movement of unrelated objects. Taking inspiration from this, to enable manipulators to perform accurate grasping tasks in complex environments, we propose a new data-driven grasping approach for robotic dexterous hands: the push-fit grasping approach. This method combines human grasping with model-free deep reinforcement learning to realize the robot's collaborative grasping ability. An end-to-end conditional converter grasping network is first trained, which takes the visual point cloud input to the action output. We use DQN algorithms for strategy training, further enhanced by fine-tuning methods for real-world learning. The experimental results show that the push strategy we learned significantly improves the grasping performance, and the success rate of cooperative grasping is increased by 8 %. It is worth noting that this fine-tuning method significantly reduces the actual training time, with the real world training taking up only one-fifth of the time in the simulated world. Even in demanding scenarios characterized by confusion and complexity, it facilitates rapid learning. This study provides new insights for effectively addressing complex challenges involving multi-fingered dexterous hands and human-computer interaction. Our code is available in the github repository.