Robot Arm Research Articles

Full‐vdW Heterosynaptic Memtransistor with the Ferroelectric Inserted Functional Layer and its Neuromorphic Applications

AbstractThe emergence of 2D van der Waals (vdW) materials, owing to highly tunable electrical conductivity, remarkably free stackability, and excellent compatibility for heterojunction integration, has provided abundant research diversity of artificial synapses. Here, an innovative 2D vdW heterosynaptic memtransistor (vdW‐HT) synapse is proposed with a ferroelectric CuInP2S6 inserted layer. Neuromorphic synaptic weight change of the vdW‐HT synapse in this work are modulated by the synergistic effects of interlayer coupling and ferroelectric polarization reversal. It is the first time to evaluate the required initial energy consumption of ferroelectric vdW‐HT synapses with matrixed biomimetic characteristics of “Learning–Forgetting–Re‐learning–Memorizing.” The required initial energy consumption is only ≈3.06 pJ, which provided supporting evidence to indicate the promising potential of vdW‐HT synapses in exploring neuromorphic applications. A vdW‐HT computing system with artificial recognition capability for intelligent automobiles is established and demonstrated outstanding recognition abilities for multiple targets in various environments. The highest recognition accuracy for pedestrians is 98%. In addition, the excellent recognition property is integrated with a robotic arm, successfully achieving high‐precision grasping behavior and designated position transmission for identified targets. These results provide promising strategies for the integrated development of emerging neuromorphic electronics and industrial applications.

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).

Compound control method for reliability of the robotic arms with clearance joint

Compound control method for reliability of the robotic arms with clearance joint

Bio-Inspired Motion Emulation for Social Robots: A Real-Time Trajectory Generation and Control Approach.

Assistive robotic platforms have recently gained popularity in various healthcare applications, and their use has expanded to social settings such as education, tourism, and manufacturing. These social robots, often in the form of bio-inspired humanoid systems, provide significant psychological and physiological benefits through one-on-one interactions. To optimize the interaction between social robotic platforms and humans, it is crucial for these robots to identify and mimic human motions in real time. This research presents a motion prediction model developed using convolutional neural networks (CNNs) to efficiently determine the type of motions at the initial state. Once identified, the corresponding reactions of the robots are executed by moving their joints along specific trajectories derived through temporal alignment and stored in a pre-selected motion library. In this study, we developed a multi-axial robotic arm integrated with a motion identification model to interact with humans by emulating their movements. The robotic arm follows pre-selected trajectories for corresponding interactions, which are generated based on identified human motions. To address the nonlinearities and cross-coupled dynamics of the robotic system, we applied a control strategy for precise motion tracking. This integrated system ensures that the robotic arm can achieve adequate controlled outcomes, thus validating the feasibility of such an interactive robotic system in providing effective bio-inspired motion emulation.

Hybrid-3D robotic suite in spine and trauma surgery - experiences in 210 patients

BackgroundIn modern Hybrid ORs, the synergies of navigation and robotics are assumed to contribute to the optimisation of the treatment in trauma, orthopaedic and spine surgery. Despite promising evidence in the area of navigation and robotics, previous publications have not definitively proven the potential benefits. Therefore, the aim of this retrospective study was to evaluate the potential benefit and clinical outcome of patients treated in a fully equipped 3D-Navigation Hybrid OR.MethodsProspective data was collected (March 2022- March 2024) after implementation of a fully equipped 3D-Navigation Hybrid OR (“Robotic Suite”) in the authors level 1 trauma centre. The OR includes a navigation unit, a cone beam CT (CBCT), a robotic arm and mixed reality glasses. Surgeries with different indications of the spine, the pelvis (pelvic ring and acetabulum) and the extremities were performed. Spinal and non-spinal screws were inserted. The collected data was analysed retrospectively. Pedicle screw accuracy was graded according to the Gertzbein and Robbins (GR) classification.ResultsA total of n = 210 patients (118 m:92f) were treated in our 3D-Navigation Hybrid OR, with 1171 screws inserted. Among these patients, 23 patients (11.0%) arrived at the hospital via the trauma room with an average Injury Severity Score (ISS) of 25.7. There were 1035 (88.4%) spinal screws inserted at an accuracy rate of 98.7% (CI95%: 98.1-99.4%; 911 GR-A & 111 GR-B screws). The number of non-spinal screws were 136 (11.6%) with an accuracy rate of 99.3% (CI95%: 97.8-100.0%; 135 correctly placed screws). This resulted in an overall accuracy rate of 98.8% (CI95%: 98.2-99.4%). The robotic arm was used in 152 cases (72.4%), minimally invasive surgery (MIS) was performed in 139 cases (66.2%) and wound infection occurred in 4 cases (1,9%). Overall, no revisions were needed.ConclusionBy extending the scope of application, this study showed that interventions in a fully equipped 3D-Navigation Hybrid OR can be successfully performed not only on the spine, but also on the pelvis and extremities. In trauma, orthopaedics and spinal surgery, navigation and robotics can be used to perform operations with a high degree of precision, increased safety, reduced radiation exposure for the OR-team and a very low complication rate.

Path Planning Based on Artificial Potential Field with an Enhanced Virtual Hill Algorithm

The artificial potential field algorithm has been widely applied to mobile robots and robotic arms due to its advantage of enabling simple and efficient path planning in unknown environments. However, solving the local minimum problem is an essential task and is still being studied. Among current methods, the technique using the virtual hill concept is reliable and suitable for real-time path planning because it does not create a new local minimum and provides lower complexity. However, in the previous study, the shape of the obstacles was not considered in determining the robot’s direction at the moment it is trapped in a local minimum. For this reason, longer or blocked paths are sometimes selected. In this study, we propose an enhanced virtual hill algorithm to reduce errors in selecting the driving direction and improve the efficiency of robot movement. In the local minimum area, a dead-end algorithm is also proposed that allows the robot to return without entering deeply when it encounters a dead end.

Research on an Identification and Grasping Device for Dead Yellow-Feather Broilers in Flat Houses Based on Deep Learning

The existence of dead broilers in flat broiler houses poses significant challenges to large-scale and welfare-oriented broiler breeding. To ensure the timely identification and removal of dead broilers, a mobile device based on visual technology for grasping them was meticulously designed in this study. Among the multiple recognition models explored, the YOLOv6 model was selected due to its exceptional performance, attaining an impressive 86.1% accuracy in identification. This model, when integrated with a specially designed robotic arm, forms a potent combination for effectively handling the task of grasping dead broilers. Extensive experiments were conducted to validate the efficacy of the device. The results reveal that the device achieved an average grasping rate of dead broilers of 81.3%. These findings indicate that the proposed device holds great potential for practical field deployment, offering a reliable solution for the prompt identification and grasping of dead broilers, thereby enhancing the overall management and welfare of broiler populations.

String‐Like Self‐Capacitance‐Based Proximity and Tactile Sensor that Can be Wrapped Around Robotic Arms

AbstractCooperative robots that perform safety functions as part of cooperative work with humans have been recently drawing attention. Proximity and tactile sensors that can be retrofitted to a robot's surface are important safety measures for existing robots that are not manufactured as cooperative robots to be used as cooperative robots. In this paper, a string‐like self‐capacitance‐based proximity and tactile sensor that can be easily wrapped around various types of existing robots is proposed. The novel and useful point in this paper is that it can be easily mounted on various robots by a flexible string‐like proximity and tactile sensor. The sensor module can be adjusted to the required length by connecting different numbers of modules depending on the mounting surface. As a demonstration, six prototype sensor modules were connected by each connector and wrapped around a robotic arm. The sensor modules on the robot's surface could detect an object within its proximity range and detect the rough pressure after contact. In addition, the robotic arm with the sensor modules could be controlled in real time using the data obtained from the sensor that are object detection data at proximity range. These results confirm that the proposed sensor module can be used in the proximity and tactile sensors of existing robots. © 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.

Accuracy of Reaction Time Measurement on Automated Neuropsychological Assessment Metric UltraMobile.

This observational study examined the accuracy of simple reaction time (RT) measurements on various touchscreen tablet devices using the Automated Neuropsychological Assessment Metric (ANAM) UltraMobile test battery. The study investigated the implications of interpreting ANAM UltraMobile with laptop-based normative data by analyzing the magnitude and variability of RT accuracy across devices. RT accuracy on 10 different tablets was assessed using a photodetector and robotic arm to respond to stimuli at predetermined response times. The recorded RT was compared with the true RT obtained from the robotic arm to calculate the RT error. ANAM UltraMobile recorded slower RTs than the laptop version. Additionally, RT error varied considerably among the 10 tablet models, suggesting psychometrically significant implications that could lead to interpretive errors when using laptop-based normative data. Relative to the RT error from the laptop-based version of ANAM, tablet data from ANAM UltraMobile are significantly slower and exhibit large variability between devices. These differences may have clinically significant implications for the comparability of the two versions. The findings suggest that further research with human participants is needed to assess the equivalence of ANAM UltraMobile with its predecessor.

Trajectory Planning of Robotic Arm Based on Particle Swarm Optimization Algorithm

Trajectory Planning of Robotic Arm Based on Particle Swarm Optimization Algorithm

A transcrestal sinus floor elevation strategy based on a haptic robot system: An in vitro study.

To reveal the force profiles recorded by haptic autonomous robotic force feedback during the transcrestal sinus floor elevation (TSFE) process, providing a reference for the surgery strategy during TSFE. A total of 42 maxillary sinus models with different angles of the sinus floor (30°, 40°, 50°, 60°, 70°, 80°, and 90°, compared to vertical plane) were 3D printed. Implant site preparation was performed using a robotic system, and the total force (Ft) and axial force along the drill (Fz) during the surgery were recorded by the haptic robotic arm. The actual initial breakthrough point (drill contacting sinus floor) and complete breakthrough point (drill penetrating the sinus floor) were defined visually (the actual IBP and the actual CBP). The theoretical initial breakthrough point (the theoretical IBP) and the theoretical complete breakthrough point (the theoretical CBP) defined by the robot-guided system and the CBCT were determined by real-time force feedback and imaging distance measurement, respectively. The distance from the bottom of the resin model to the actual IBP and the actual CBP was defined as Di and Dt, respectively. The difference in Fz began to increase significantly at 70°, while the difference in Ft became significant at 60°. When the angle was greater than 70°, there was no significant difference in the discrepancy between the actual and theoretical perforation points. Compared to judging the breakthrough point by CBCT, real-time force feedback TSFE under robotic surgery achieved more accurate initial breakthrough point detection. The smaller the angle, the larger the breakthrough force for the drill. The real-time force feedback of haptic robotic system during TSFE could provide reliable reference for dentists. More clinical studies are needed to further validate the application of robotic surgery assisted TSFE.

Collaborative robot acting as scrub nurse for cataract surgery (CRASCS).

Worldwide, healthcare systems are struggling to tackle a nursing shortage. As per the data released by the American Hospital Association, the healthcare workforce could face a loss of around 5 lakh nurses by the end of the year. Consequently, it could result in those deficiencies, which will be 1.1 million instead of 0.6 million. The current nursing scenario in India as per the Indian Nursing Council (INC), which is a board under the ministry and is responsible for legally confirming and maintaining universal standardized training for nursing, is that the 1.96 nurses out of every 1000 Indians that are there are much behind the World Health Organization's (WHO) recommended figure of 3 nurses per 1000. To mitigate the nurse shortage, a collaborative robotic system was designed that can assist with surgical procedures with a collaborative robot acting as a scrub nurse for cataract surgery (CRASCS) represented in Fig. 1. Accordingly, the model has been built to empower a customized 3d printed 5-Degree of freedom robotic arm by tracking the phase of surgery in real-time and automatically supplying the clinician with the ideal equipment that is needed for the particular phase of surgery. The system is supported with one more model which can identify where the surgical equipment is located within the arm range. The system is also supported with voice commands which help in picking up the right surgical equipment in the middle of any phase of the surgery. In this way, the system could be able to potentially handle the shortage of surgical nurses around the world and benefit humanity.

Developing a real-time hand exoskeleton system that controlled by a hand gesture recognition system via wireless sensors

Numerous studies have been conducted using wearable sensors on gesture recognition and classification methods for the control of robotic arms, prostheses, and exoskeleton systems. The primary goal of this study is to classify 32 different hand gestures in real-time using electromyography (EMG) signals. EMG signals obtained by measuring the electrical activity of the muscles were simulated wirelessly on a hand exoskeleton. A Myo Armband placed on the upper arm was used to generate these signals for evaluating and validating the system. The signals were normalized according to maximum voluntary contraction (MVC), and relevant features such as MAV, STD, VAR, RMS, IEMG, ZC, and WL were selected using the univariate feature selection. A support vector machine (SVM) was then used for classification. The 32 different hand gestures were classified to an accuracy level of 99.9%. In short, this proposed approach can be effectively used in real-time hand gesture recognition and generating real-time actions for the hand exoskeleton system.

3D reconstruction and volume measurement of irregular objects based on RGB-D camera

Abstract To address the challenge of measuring volumes of irregular objects, this paper proposes a volume measurement method based on 3D point cloud reconstruction. The point clouds of the object with multiple angles are obtained from an RGB-D camera mounted on a robotic arm, and then are reconstructed to form a whole complete point cloud to calculate the volume of the object. Firstly, the robotic arm is controlled to move to four angles for capturing the original point clouds of the target. Then, by using the rotation and translation matrices obtained from the calibration block pre-registration, the point clouds data from the four angles are fused and reconstructed. Subsequently, the issue of missing bottom point cloud data is addressed using a bottom-filling algorithm. Following this, the efficiency of the point cloud volume calculation algorithm is enhanced through the application of AABB filtering. Finally, the reconstructed point cloud volume is calculated using a slicing algorithm that integrates 2D point cloud segmentation and point cloud sorting. Experimental results show that this method achieves a volume measurement accuracy of over 95% for irregular objects and exhibits good robustness.

Better restoration of joint line obliquity in tibia first restricted kinematic alignment versus mechanical alignment TKA.

In total knee arthroplasty (TKA), suboptimal restoration of joint line obliquity (JLO) and joint line height (JLH) may lead to diminished implant longevity, increased risk of complications, and reduced patient reported outcomes. The primary objective of this study is to determine whether restricted kinematic alignment (rKA) leads to improved restoration of JLO and JLH compared to mechanical alignment (MA) in TKA. This retrospective study assessed patients who underwent single implant design TKA for primary osteoarthritis, either MA with manual instrumentation or rKA assisted with imageless navigation robotic arm TKA. Pre- and post-operative long standing AP X-ray imaging were used to measure JLO formed between the proximal tibial joint line and the floor. JLH was measured as the distance from the femoral articular surface to the adductor tubercle. Overall, 200 patients (100 patients in each group) were included. Demographics between the two groups including age, sex, ASA, laterality, and BMI did not significantly differ. Distribution of KL osteoarthritis classification was similar between the groups. For the MA group, pre- to post-operative JLO significantly changed (2.94° vs. 2.31°, p = 0.004). No significant changes were found between pre- and post-operative JLH (40.6mm vs. 40.6mm, p = 0.89). For the rKA group, no significant changes were found between pre- and post-operative JLO (2.43° vs. 2.30°, p = 0.57). Additionally, no significant changes were found between pre- and post-operative JLH (41.2mm vs. 42.4mm, p = 0.17). Pre- to post-operative JLO alteration was five times higher in the MA group compared to the rKA group, although this comparison between groups did not reach statistical significance (p = 0.09). rKA-TKA results in high restoration accuracy of JLO and JLH, and demonstrates less pre- and post-operative JLO alteration compared to MA-TKA. With risen interest in joint line restoration accuracy with kinematic alignment, these findings suggest potential advantages compared to MA. Future investigation is needed to correlate between joint line restoration accuracy achieved by rKA and enhanced implant longevity, reduced risk of post-operative complications, and heightened patient satisfaction.

Restoration of grasping in an upper limb amputee using the myokinetic prosthesis with implanted magnets.

The loss of a hand disrupts the sophisticated neural pathways between the brain and the hand, severely affecting the level of independence of the patient and the ability to carry out daily work and social activities. Recent years have witnessed a rapid evolution of surgical techniques and technologies aimed at restoring dexterous motor functions akin to those of the human hand through bionic solutions, mainly relying on probing of electrical signals from the residual nerves and muscles. Here, we report the clinical implementation of an interface aimed at achieving this goal by exploiting muscle deformation, sensed through passive magnetic implants: the myokinetic interface. One participant with a transradial amputation received an implantation of six permanent magnets in three muscles of the residual limb. A truly self-contained myokinetic prosthetic arm embedding all hardware components and the battery within the prosthetic socket was developed. By retrieving muscle deformation caused by voluntary contraction through magnet localization, we were able to control in real time a dexterous robotic hand following both a direct control strategy and a pattern recognition approach. In just 6 weeks, the participant successfully completed a series of functional tests, achieving scores similar to those achieved when using myoelectric controllers, a standard-of-care solution, with comparable physical and mental workloads. This experience raised conceptual and technical limits of the interface, which nevertheless pave the way for further investigations in a partially unexplored field. This study also demonstrates a viable possibility for intuitively interfacing humans with robotic technologies.

Microstructure-Reconfigured Graphene Oxide Aerogel Metamaterials for Ultrarobust Directional Sensing at Human-Machine Interfaces.

Graphene aerogels hold huge promise for the development of high-performance pressure sensors for future human-machine interfaces due to their ordered microstructure and conductive network. However, their application is hindered by the limited strain sensing range caused by the intrinsic stiffness of the porous microstructure. Herein, an anisotropic cross-linked chitosan and reduced graphene oxide (CCS-rGO) aerogel metamaterial is realized by reconfiguring the microstructure from a honeycomb to a buckling structure at the dedicated cross-section plane. The reconfigured CCS-rGO aerogel shows directional hyperelasticity with extraordinary durability (no obvious structural damage after 20 000 cycles at a directional compressive strain of ≤0.7). The CCS-rGO aerogel pressure sensor exhibits an ultrahigh sensitivity of 121.45 kPa-1, an unprecedented sensing range, and robust mechanical and electrical performance. The aerogel sensors are demonstrated to monitor human motions, control robotic hands, and even integrate into a flexible keyboard to play music, which opens a wide application potential in future human-machine interfaces.

A passive convex optimal control algorithm for teleoperating a redundant robotic arm in minimally invasive surgery

AbstractIn recent years, the remote center of motion (RCM) constraint has moved from purely mechanical to software implementation, enabling the use of serial manipulators in robotic‐assisted minimal invasive surgery. However, ensuring safety with software‐based RCM presents challenges. This article addresses this issue by introducing a novel control algorithm for a 7‐DOF redundant robotic arm, taking into account a software RCM while considering system passivity and kinematic constraints. The algorithm is both a control optimizer and a robot kinematics inversion, enabling precise and dexterous control for various surgical tasks and other control applications. By formulating a quadratic optimization problem under linear constraints, the algorithm guarantees the robotic arm's performance, safety, and stability under communication delay. Experimental validation demonstrates the effectiveness and accuracy of the control algorithm in executing a surgical training task (peg‐and‐ring) during bilateral teleoperation. The results highlight the successful implementation of the RCM constraint, ensuring patient safety and optimizing the manipulation capabilities of the robotic arm.

Spring-reinforced pneumatic actuator and soft robotic applications

Abstract Compared to rigid-structure robots, soft robots possess higher degrees of freedom and stronger environmental adaptability, which has aroused increasing attention in the robotic field. Among them, soft pneumatic robots have excellent performances in various practical applications. However, the nonlinearity and instability of pressure response of soft actuators caused by lateral expansion come to a great challenge. To address this problem, we proposed to embed a spring constraint layer around each single air chamber. Following the design concept, we obtained single-cavity and multi-DoF pneumatic actuators and evaluated their elongation and bending characteristics. Experimental results demonstrated that our proposed actuators have more linear pressure response as well as higher consistency. Eventually, through robotic applications, including soft robotic hand and gripper our proposed actuators could facilitate flexible manipulation and elaborate performance.

Robotic colorectal resections are associated with less postoperative pain, decreased opioid use, and earlier return to work as compared to the laparoscopic approach.

While robotic and laparoscopic surgeries are both minimally invasive in nature, they are intrinsically different approaches and it is critical to understand outcome differences between the two. Studies evaluating pain outcomes and opioid requirement differences between the robotic and laparoscopic colorectal resections are conflicting and often underpowered. In this retrospective, cohort study, we compare postoperative opioid requirements, reported as morphine milligram equivalents (MME), postoperative average and highest pain scores across postoperative days (POD) 0-5, and return to work in patients who underwent robotic versus laparoscopic colorectal resections. The sample size was selected based on power calculations. Daily pain scores and MME were used as outcomes in linear mixed effect models with unstructured covariance between time points. Propensity score weighting was used to adjust for imbalances. Patients in the robotic group required significantly less opioids as measured by MME on all postoperative days (p = 0.004), as well as lower average and highest daily pain scores for POD 0-5 (p = 0.02, and p = 0.006, respectively). In a linear mixed-effects model, robotic resections were associated with a decrease in average pain scores by 0.36 over time (p = 0.03) and 35 fewer MME requirements than the laparoscopic group (p = 0.0004). Patients in the robotic arm had earlier return to work (2.1 vs 3.8days, p = 0.036). The robotic approach to colorectal resections is associated with significantly less postoperative pain, decreased opioid requirements, and earlier return to work when compared to laparoscopy, suggesting that the robotic platform provides important clinical advantages overthe laparoscopic approach.