Control Of Robot Arm Research Articles

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

Non-invasive brain-machine interface control with artificial intelligence copilots.

Motor brain-machine interfaces (BMIs) decode neural signals to help people with paralysis move and communicate. Even with important advances in the last two decades, BMIs face key obstacles to clinical viability. Invasive BMIs achieve proficient cursor and robotic arm control but require neurosurgery, posing significant risk to patients. Non-invasive BMIs do not have neurosurgical risk, but achieve lower performance, sometimes being prohibitively frustrating to use and preventing widespread adoption. We take a step toward breaking this performance-risk tradeoff by building performant non-invasive BMIs. The critical limitation that bounds decoder performance in non-invasive BMIs is their poor neural signal-to-noise ratio. To overcome this, we contribute (1) a novel EEG decoding approach and (2) artificial intelligence (AI) copilots that infer task goals and aid action completion. We demonstrate that with this "AI-BMI," in tandem with a new adaptive decoding approach using a convolutional neural network (CNN) and ReFIT-like Kalman filter (KF), healthy users and a paralyzed participant can autonomously and proficiently control computer cursors and robotic arms. Using an AI copilot improves goal acquisition speed by up to 4.3 × in the standard center-out 8 cursor control task and enables users to control a robotic arm to perform the sequential pick-and-place task, moving 4 randomly placed blocks to 4 randomly chosen locations. As AI copilots improve, this approach may result in clinically viable non-invasive AI-BMIs.

A Koopman-based residual modeling approach for the control of a soft robot arm

Soft robots are challenging to model and control due to their poorly defined kinematics and nonlinear dynamics. Recently, Koopman operator theory has been shown capable of constructing control-oriented soft robot models from data. However, building these models requires extensive data collection and they do not necessarily generalize well outside of the training observations. This paper presents a more data-efficient and generalizable approach to soft robot modeling that first identifies a physics-based Koopman model then supplements it with a data-driven residual Koopman model. The resulting combined model is linear and thus compatible with real-time model-based control techniques such as Model Predictive Control (MPC). The efficacy of the approach is demonstrated on several simulated systems and on a real soft robot arm, where it is shown to generate models that are more accurate than purely physics-based models and require less data to construct than purely data-driven models. Using a model-based controller, the soft arm is able to successfully track end effector trajectories, perform a pick-and-place task, and write on a dry-erase board, showcasing the applicability of this framework to increase the capabilities of soft robotic systems.

Rapid-Learning Collaborative Pushing and Grasping via Deep Reinforcement Learning and Image Masking

When multiple objects are positioned close together or stacked, pre-grasp operations such as pushing objects can be used to create space for the grasp, thereby improving the grasping success rate. This study develops a model based on a deep Q-learning network architecture and introduces a fully convolutional network to accurately identify pixels in the workspace image that correspond to target locations for exploration. In addition, this study incorporates image masking to limit the exploration area of the robotic arm, ensuring that the agent consistently explores regions containing objects. This approach effectively addresses the sparse reward problem and improves the convergence rate of the model. Experimental results from both simulated and real-world environments show that the proposed method accelerates the learning of effective grasping strategies. When image masking is applied, the success rate in the grasping task reaches 80% after 600 iterations. The time required to reach 80% success rate is 25% shorter when image masking is used compared to when it is not used. The main finding of this study is the direct integration of image masking technique with a deep reinforcement learning (DRL) algorithm, which offers significant advancement in robotic arm control. Furthermore, this study shows that image masking technique can substantially reduce training time and improve the object grasping success rate. This innovation enables the robotic arm to better adapt to scenarios that conventional DRL methods cannot handle, thereby improving training efficiency and performance in complex and dynamic industrial applications.

Design of an Optimal Multivariable PID Controller for a Two-Link Robot Arm

Incorporating manipulator robots into various industries has revolutionized automation and manufacturing processes. Manipulator robots are versatile machines equipped with robotic arms and end-effectors, enabling them to handle complex tasks with precision and efficiency. This research aims to design an optimal Mutivariable PID (MPID) controller that ensures precise and accurate movements of two-link robot arm. Theoretical foundations of the MPID controller are discussed together with its relevance to robotic arm systems, and the challenges associated with its implementation. The research methodology involves mathematical modelling of the two-link robot arm dynamics, and the use of an optimization algorithm to determine the optimal MPID controller coefficients. Simulation results demonstrate performance enhancement and productivity of the two-link robot arm through the improved MPID controller.

Human–robot interaction via wearable device—a wireless glove system for remote control of 7-DoF robotic arm

Human–robot interaction via wearable device—a wireless glove system for remote control of 7-DoF robotic arm

Deep Learning-Driven Robot Arm Control Fusing Convolutional Visual Perception and Predictive Modeling for Motion Planning

The wide application of robotic technology in various industries from industrial automation to medical assistance is gradually changing our production and lifestyle, and has attracted widespread attention in many fields. However, existing robotic control systems often grapple with limited flexibility and poor adaptability to complex environments, particularly in highly dynamic operational contexts. Against this backdrop, the integration of deep learning technologies offers new possibilities in enhancing robotic perception and decision-making, especially in visual perception and motion planning. To address these challenges, we have introduced a novel robotic arm control network model, MPC-WGAN-Faster R-CNN, which combines Model Predictive Control concepts with Wasserstein Generative Adversarial Networks and Faster R-CNN visual recognition technology. This integration aims to improve the precision and adaptability of robotic arm operations in complex environments.

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).

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.

Low modulus hydrogel-like elastomer sensors with ultra-fast self-healing, underwater self-adhesion, high durability/stability and recyclability for bioelectronics

Flexible sensors simultaneously with property of hydrogel-like low modulus/room temperature ultra-fast self-healing and with elastomer-like durability /environmental stability and underwater adhesion for bioelectronics has not been reported. A low modulus hydrogel-like elastomer that achieves ultrafast self-healing through molecular chain entanglement at room temperature was prepared based on furfuryl alcohol-modified poly(sebacate glyceride) (PGS) prepolymer, furfuryl alcohol-modified poly(ionic liquid) and bismaleimide by Diels-Alder (DA) reaction. The conductive elastomer-based flexible sensors exhibit hydrogel-like properties of low modulus (6.41 kPa) and ultra-fast self-healing (98 % self-healing efficiency within 5 s). The elastomer also possesses rapid subzero and underwater self-healing properties within 5 s. Moreover, PGS-0.2DA-0.2PIL exhibits pressure sensitive adhesive properties and can be adhered/re-adhered in water. The flexible sensor shows elastomer-like high durability, high environmental stability, multiple recyclability and reusability, and it exhibits wide detection ranges, fast response time, low hysteresis, anti-freezing, anti-bacterial and good biocompatibility. The flexible sensors can accurately identify micro-expressions/eye rotation, monitor human movement/health, detect ECG/EMG signals and control robotic arm movements. In conclusion, a new strategy for design of hydrogel-like conductive elastomers via molecular structure design is proposed, and the elastomers-based flexible sensors with low modulus, rapid self-healing and durability/environmental stability show great promising for bioelectronic applications.

Development of robotic arm control using Arduino controller

The advance of Arduino-based technology has spurred innovation in the realm of robotic arm control, offering a cost-effective and accessible platform for enthusiasts and professionals alike. This paper presents the development of robot arm control using an Arduino controller. The work involves the integration of Arduino microcontrollers and sensors to enable precise and dynamic control of a robotic arm. The proposed robot is controlled by 4 servo motors, the motors rotate left, right, front, and back. The paper discusses the challenges encountered during the development process and proposes solutions, paving the way for further advancements in this burgeoning field. With Arduino's widespread availability and affordability, the presented robotic arm control system holds promise for expanding the accessibility of robotics education and fostering innovation in automation technologies. This paper provides a glimpse into the promising synergy between Arduino and robotic arm control, highlighting the contributions and implications of this technology in shaping the future of automation.

3D Model Prototype of Robotic Arm and Motion Control Simulation on 4-DOF Based on Virtual Reality

Penggunaan robotika dalam industri modern telah menjadi krusial, terutama dalam otomatisasi dan manufaktur. Robot lengan, sebagai elemen kunci, memerlukan pengembangan dan pengujian prototipe yang teliti untuk mencapai performa optimal. Teknologi Virtual Reality (VR) muncul sebagai solusi inovatif untuk mensimulasikan gerakan robot lengan di lingkungan virtual, memungkinkan visualisasi dan interaksi mendalam dalam berbagai derajat kebebasan (DOF). Penelitian ini bertujuan untuk membangun prototype model robot lengan 3D dan mengerjakan eksplorasi virtualisasi gerakan dari pengembangan prototipe robot lengan berbasis VR. Hasil penelitian ini diantaranya telah dilakukan integrasi teknologi VR dengan sistem kontrol robot lengan dalam lingkungan Unity, memungkinkan pengguna mengendalikan gerakan robot lengan dalam ruang 3D secara real-time. Studi ini menunjukkan bahwa robot lengan dengan 4 DOF dapat mencapai fleksibilitas dan presisi yang diinginkan pada skenario uji yang telah dirancang. Dari hasil pengujian antara parameter simulasi dari lingkungan unity dan perhitungan secara teoritis gerakan robot lengan yang dikendalikan menggunakan controller, diperoleh rentang koordinat yang dijangkau adalah x = -19,85 cm hingga 28,53 cm, y = 21,69 cm hingga 52,04 cm, z = -15,35 cm hingga -24,87 cm. Setelah dilakukan perhitungan perbandingan antara hasil pengujian di Unity dan hasil perhitungan berdasarkan rumus, didapatkan selisih koordinat sebesar x = 0,080, y = 0,038, dan z = 0,034

The supernumerary robotic limbs of brain-computer interface based on asynchronous steady-state visual evoked potential

Brain-computer interface (BCI) based on steady-state visual evoked potential (SSVEP) have attracted much attention in the field of intelligent robotics. Traditional SSVEP-based BCI systems mostly use synchronized triggers without identifying whether the user is in the control or non-control state, resulting in a system that lacks autonomous control capability. Therefore, this paper proposed a SSVEP asynchronous state recognition method, which constructs an asynchronous state recognition model by fusing multiple time-frequency domain features of electroencephalographic (EEG) signals and combining with a linear discriminant analysis (LDA) to improve the accuracy of SSVEP asynchronous state recognition. Furthermore, addressing the control needs of disabled individuals in multitasking scenarios, a brain-machine fusion system based on SSVEP-BCI asynchronous cooperative control was developed. This system enabled the collaborative control of wearable manipulator and robotic arm, where the robotic arm acts as a "third hand", offering significant advantages in complex environments. The experimental results showed that using the SSVEP asynchronous control algorithm and brain-computer fusion system proposed in this paper could assist users to complete multitasking cooperative operations. The average accuracy of user intent recognition in online control experiments was 93.0%, which provides a theoretical and practical basis for the practical application of the asynchronous SSVEP-BCI system.

History and trends of robot-assisted spine surgery

Spanning two decades since the 1st generation spinal robotics inception, the robot-assisted spine surgery (RSS) technology has evolved through generations, culminating in the 4th generation characterized by real-time visual navigation and wire-free screw placement. The fundamental principles of RSS technology include surgical planning, tracking, image registration, and robotic arm control technologies. Currently, RSS technology is maturely employed in thoracolumbar procedures and is progressively being applied in cervical surgeries, spinal tumor resections, and percutaneous operations, offering advantages in reducing tissue trauma and exposure to radiation, thereby improving patient outcomes. Emerging research also focuses on the cost-effectiveness of clinical applications and robot-specific complications. With the integration of artificial intelligence into surgical planning, RSS technology is poised to further incorporate emerging technologies and expand its application across a broader clinical spectrum.

Development and application of a mechanical arm-based in situ 3D bioprinting method for the repair of skin wounds

Current treatments for skin wounds typically involve multiple surgical procedures that require complex processes and expensive costs, making it difficult to achieve timely treatment in field environments. We developed an innovative in situ printing method, utilizing robotic arm control, to address the significant challenges of large-scale skin wound repair resulting from natural disasters such as earthquakes, fires, and explosions during relief efforts. Our portable 3D printing equipment, which integrates debridement, precise 3D scanning and modeling of wounds, and compatibility with cell-loaded bioink, facilitates rapid repair of large-area skin wounds in specialized field environments. Compared with traditional methods, this in situ printing method has significant advantages, including the ability to customize treatment according to the unique needs of the wound, achieve rapid healing, and the potential to reduce the total cost. We conducted experiments on rats with full-thickness dorsal skin defects and compared the performance of in situ bioprinting method with commercial skin defect repair dressings. Our results demonstrate that the in situ bioprinted skin achieved faster wound healing and more uniform re-epithelialization than the commercial dressing treatment. This study demonstrates the potential of in situ bioprinting method as a promising and effective strategy for rapid skin wound healing, especially for patients in remote environments where traditional wound treatment methods may not be readily available or practical.

An improved preconditioned conjugate gradient method for unconstrained optimization problem with application in Robot arm control

AbstractThis work suggests improved conjugate gradient methods for enhancing the efficiency and robustness of the classical conjugate gradient methods. The study modifies the diagonal of the inverse Hessian approximation of the Broyden–Fletcher–Goldfarb–Shanno (BFGS) quasi‐Newton update in order to build a preconditioner for nonlinear conjugate gradient (NCG) methods applied to large‐scale unconstrained optimization problems. Damping techniques were embedded into the algorithm to impose the positive definiteness of the diagonal approximation. This made the methods easy to use and presented a viable way to increase the effectiveness of unconstrained optimization techniques. Experimental findings from a collection of benchmark problems demonstrate the efficiency and robustness of the proposed method when compared to five other existing NCG algorithms. Moreover, the successful application of the algorithm to manipulate robotic planar motion control systems with 3 degrees of freedom has been demonstrated, highlighting the practicality of the proposed approach.

Optimizing robotic arm control using deep Q-learning and artificial neural networks through demonstration-based methodologies: A case study of dynamic and static conditions

This paper uses robot programming techniques, such as Deep Q Network, Artificial Neural Network, and Artificial Deep Q Network, to address challenges related to controlling robotic arms through demonstration learning. Static and dynamic states of the subjects were the subjects of experiments. Each method's classification accuracy process success values and experimental condition combination were evaluated. The DQN method demonstrated favourable classification accuracy outcomes, achieving an Accuracy value of 0.64 for the fixed dice and 0.52 for the moving dice. The Response value was 0.51 for the fixed dice and 0.41 for the moving dice, indicating a moderate level. The ANN method demonstrated lower accuracy, with Accuracy values of 0.59 and 0.56 and Response values of 0.61 and 0.58, respectively. The ADQN method demonstrated superior outcomes, with Accuracy values of 0.66 and 0.59 and Response values of 0.67 and 0.61. During the initial learning iterations, ADQN demonstrated the highest success rate at 33.67 %, whereas DQN and ANN achieved 28.39 % and 20.13 % success rates, respectively. As the number of iterations increased, all methods demonstrated improvement in their results. ADQN maintained a high success rate of 97.59 %, while DQN and ANN attained 82.16 % and 88.66 %, respectively. As the number of iterations increases, the results of all methods improve, but the success rate of the Artificial Deep Q Network remains high. As the number of iterations increases, both Deep Q Network and Artificial Neural Network demonstrate the potential to achieve good results. Overall, the findings support the efficacy of robot programming techniques that incorporate demonstration learning. The Artificial Deep Q Network is the most successful and fast-converging method suitable for various robot control tasks. These findings provide a foundation for future research and large-scale, comprehensive learning applications for complex rot control.

User acceptance as key factor for development and risk management of an AI-based robot arm control

User acceptance as key factor for development and risk management of an AI-based robot arm control

An obstacle avoidance method for robotic arm based on reinforcement learning

Purpose Robotic arms play a crucial role in various industrial operations, such as sorting, assembly, handling and spraying. However, traditional robotic arm control algorithms often struggle to adapt when faced with the challenge of dynamic obstacles. This paper aims to propose a dynamic obstacle avoidance method based on reinforcement learning to address real-time processing of dynamic obstacles. Design/methodology/approach This paper introduces an innovative method that introduces a feature extraction network that integrates gating mechanisms on the basis of traditional reinforcement learning algorithms. Additionally, an adaptive dynamic reward mechanism is designed to optimize the obstacle avoidance strategy. Findings Validation through the CoppeliaSim simulation environment and on-site testing has demonstrated the method's capability to effectively evade randomly moving obstacles, with a significant improvement in the convergence speed compared to traditional algorithms. Originality/value The proposed dynamic obstacle avoidance method based on Reinforcement Learning not only accomplishes the task of dynamic obstacle avoidance efficiently but also offers a distinct advantage in terms of convergence speed. This approach provides a novel solution to the obstacle avoidance methods for robotic arms.

Control Robotic Arm Workstation Using IoT System for Education

Robotics play an important role in the era of modern technology. Robots can be defined as mechanical devices programmed to perform manipulative tasks under automatic control. Robotic arm is usually used in conjunction with aids such as machines and fixtures. As advancement in robot technology have become an integral part of automation in the manufacturing process. This research gives an idea of how robotic arm can be controlled using internet systems. Therefore, it also a way to reduce the risk of humans being in dangerous areas. The emergence of technology has managed to witness the evolvement of a new era known as the Internet of Things (IoT). The purpose of the research is to control robotic arm workstation using IoT system for education. The research is use Arduino uno Wi-Fi microcontroller and blink application to control IoT system. By using Arduino microcontroller, the robotic arm can directly connect to the internet. Therefore, the implementation of control robotic arm using IoT based on internet connectivity of students.