Control Of Robot Arm Research Articles

Persistent Human–Machine Interfaces for Robotic Arm Control Via Gaze and Eye Direction Tracking

Persistent Human–Machine Interfaces for Robotic Arm Control Via Gaze and Eye Direction Tracking

Enhancing Robotic Arm Performance: Integrating Arduino Control and Aerodynamic Principles for 6 Degrees of Freedom

This review paper explores the integration of Arduino control and aerodynamic principles to enhance the performance of 6 Degrees of Freedom (DOF) robotic arms. Robotic arms have become indispensable in numerous industries, requiring increased precision, efficiency, and adaptability. By leveraging the capabilities of Arduino microcontrollers and incorporating aerodynamic principles, researchers and engineers can optimize the design, control algorithms, and energy efficiency of robotic arms. This paper provides an overview of Arduino control and its advantages for robotic arm control, as well as an exploration of aerodynamic principles applicable to robotic arm systems. It investigates the potential benefits of integrating Arduino control and aerodynamics, such as improved stability, reduced energy consumption, and enhanced speed. Challenges and limitations of the proposed approach are also discussed. Through a comprehensive review of existing literature, case studies, and experimental results, this paper highlights the effectiveness of integrating Arduino control and aerodynamic principles to enhance the performance of 6 DOF robotic arms. The findings contribute to the advancement of robotic arm technology and offer insights for future research and development in this field.

Real Time Human Body Posture Analysis Using Deep Learning

Abstract: We present a novel approach for accurately estimating the pose of objects in a low-cost and resource-efficient manner, making it suitable for deployment on embedded systems. Our algorithm comprises of two primary stages: object detection and spatial reconstruction. In the first stage, we employ a Convolutional Neural Network (CNN) called PoseNet for object detection. This approach has proven to be effective in detecting and localizing objects in an image. Next, utilizing stereo correspondences, we 3D reconstruct the spatial coordinates of multiple ORB features within the object's bounding box. This enables us to accurately estimate the position of the object in space. To calculate the final position of the object, we compute a weighted average of the stereo-corresponded key points' spatial coordinates. The weights are proportional to the level of ORB stereo matching, which enables us to obtain a more accurate estimate of the object's position in space. Our algorithm was tested in a calibrated environment, and we compared the results with a deep learning-based method using various datasets. The results show that our approach outperforms existing methods in terms of accuracy, while maintaining a low cost and efficient resource utilization. Our proposed method has several applications, including the quantitative and qualitative analysis of human posture. By analyzing all aspects of a person's posture, we can determine if there are any postural deviations, imbalances, or muscle weaknesses that may be causing pain or discomfort. This information can then be used to develop personalized rehabilitation programs, reducing the risk of injury and enhancing athletic performance. Furthermore, our approach can be used in various assistive technology applications, such as the control of robotic arms for pick-andplace tasks. The low-cost and resource-efficient nature of our algorithm make it ideal for deployment in embedded systems, enabling us to develop affordable and accessible assistive technology solutions. In conclusion, our proposed algorithm provides an accurate, low-cost, and resource-efficient solution for pose estimation, with a wide range of potential applications in human posture analysis, assistive technology, and beyond.

An intelligent fuzzy-particle swarm optimization supervisory-based control of robot manipulator for industrial welding applications

The propensity of manufacturers to produce goods at affordable cost, with more accuracy, and at a faster rate force them to search for novel solutions, such as deploying robots in place of people in a sector that can accommodate their needs. Welding is one of the most crucial processes in the automotive industry. This process is time-consuming, subject to error, and demands skilled professionals. The robotic application can improve this area of production and quality. Other industries, such as painting and material handling, can also profit from the use of robots. This work describes the fuzzy DC linear servo controller, which functions as a robotic arm actuator. Robots have been widely employed in most productive sectors in recent years, including assembly plates, welding, tasks at higher temperatures, etc. Controlling a robot accurately is a difficult undertaking as a robot is very nonlinear with many joints that are often organized and unstructured. To carry out the effective task, an effective PID control based on fuzzy logic has been employed together with the method of Particle Swarm Optimization (PSO) approach for the estimate of the parameter. This offline technique determines the lowest number of optimal robotic arm control parameters. To verify the controller design with computer simulation, a comparative assessment of controllers is given by means of a fuzzy surveillance controller with PSO which improves the parameter gain to provide a rapid climb, a smaller overflow, no steady condition error signal, and effective torque control of the robot arm.

Object Grasp Control of a 3D Robot Arm by Combining EOG Gaze Estimation and Camera-Based Object Recognition.

The purpose of this paper is to quickly and stably achieve grasping objects with a 3D robot arm controlled by electrooculography (EOG) signals. A EOG signal is a biological signal generated when the eyeballs move, leading to gaze estimation. In conventional research, gaze estimation has been used to control a 3D robot arm for welfare purposes. However, it is known that the EOG signal loses some of the eye movement information when it travels through the skin, resulting in errors in EOG gaze estimation. Thus, EOG gaze estimation is difficult to point out the object accurately, and the object may not be appropriately grasped. Therefore, developing a methodology to compensate, for the lost information and increase spatial accuracy is important. This paper aims to realize highly accurate object grasping with a robot arm by combining EMG gaze estimation and the object recognition of camera image processing. The system consists of a robot arm, top and side cameras, a display showing the camera images, and an EOG measurement analyzer. The user manipulates the robot arm through the camera images, which can be switched, and the EOG gaze estimation can specify the object. In the beginning, the user gazes at the screen's center position and then moves their eyes to gaze at the object to be grasped. After that, the proposed system recognizes the object in the camera image via image processing and grasps it using the object centroid. The object selection is based on the object centroid closest to the estimated gaze position within a certain distance (threshold), thus enabling highly accurate object grasping. The observed size of the object on the screen can differ depending on the camera installation and the screen display state. Therefore, it is crucial to set the distance threshold from the object centroid for object selection. The first experiment is conducted to clarify the distance error of the EOG gaze estimation in the proposed system configuration. As a result, it is confirmed that the range of the distance error is 1.8-3.0 cm. The second experiment is conducted to evaluate the performance of the object grasping by setting two thresholds from the first experimental results: the medium distance error value of 2 cm and the maximum distance error value of 3 cm. As a result, it is found that the grasping speed of the 3 cm threshold is 27% faster than that of the 2 cm threshold due to more stable object selection.

Adaptive control of cooperative robots in the presence of disturbances and uncertainties: A Bernstein–Chlodowsky approach

The function approximation technique (FAT) is a powerful mathematical tool recently utilized to design model‐free controllers for robots. However, some FAT‐based controllers depend on joint velocities, which may not be available in many real‐world applications. This problem is solved in this paper by proposing an output feedback tracking control for cooperative robotic arms using the Bernstein–Chlodowsky polynomial as an uncertainty approximator. In other words, the Bernstein–Chlodowsky approach is adopted to approximate the lumped uncertainties consisting of disturbances and unmodeled dynamics. An adaptive rule is then suggested to update the approximator's coefficients matrix. Moreover, it is assured that controlled system error signals are uniformly ultimately bounded (UUB) utilizing the Lyapunov lemma. Finally, the designed Bernstein–Chlodowsky controller is applied to a cooperative system with two arms handling a load. Besides, the results of applying the designed technique are compared with the outcomes of the Chebyshev neural network (CNN) as a state‐of‐the‐art approximation method. The simulation outcomes indicate the capability of the designed method.

An electroencephalography electrode based on a few-layer graphene/TiO2 nanotube nanoarchitecture for application in robot arm control

An electroencephalography electrode based on a few-layer graphene/TiO2 nanotube nanoarchitecture for application in robot arm control

Design of a decentralized Internet-based control system for industrial robot robotic arms

Abstract To meet the needs of small and medium-sized processing plants by establishing a software and hardware platform for a robotic arm control system with a hierarchical structure and designing a robot robotic arm motion control system that is adaptable to multiple environments and cost-effective. In this paper, the industrial robot robotic arm motion control system is designed based on the consistent fusion tracking algorithm under the decentralized Internet. While using the VisualC++ development environment for writing human-machine interface programs using the MFC framework, the device controls the robotic arm body to complete predetermined movements or operational tasks based on command information, sensing information, and the robot controller. The AMS1086CD-3.3 low-voltage differential linear regulator chip used in this design effectively implements the circuit design from 4.8V to 3.4V and successfully meets the 3.4V voltage supply required by the controller. The industrial robot robotic arm control system adopts an adaptive backstepping algorithm controller, and the error of the tracking system at 0 seconds in the simulation results is always kept within the range of [-1~1] that meets the requirements, which effectively reduces the control error of the robotic arm position. Therefore, the robotic arm control studied in this paper is composed of a complete control system in hardware and software, and the designed adaptive backstepping algorithm controller achieves the demand for good control performance of the robotic arm proper.

The Five-DOF Explosion-Removal Manipulator Based on Position and Velocity Feedforward Compensation Control.

The main problem with a robotic system arm is its sensitivity to time delays in the control process. Due to this problem, it is necessary to further optimize the control process of the system. One solution is to deal with the control accuracy and response speed issues of robotic arm joints, to improve the system's response performance and enhance the system's anti-interference ability. This paper proposes a speed feedforward and position control scheme for robotic arm joint control. The conclusion section shows that compared to traditional five-degree-of-freedom robotic arm systems, the addressed robotic arm control system has a lower tracking delay and better dynamic response performance. It can improve the system's response performance while also enhancing its anti-interference ability.

Sensor Fusion-Based Teleoperation Control of Anthropomorphic Robotic Arm

Sensor fusion is a technique that combines information from multiple sensors in order to improve the accuracy and reliability of the data being collected. In the context of teleoperation control of an anthropomorphic robotic arm, sensor fusion technology can be used to enhance the precise control of anthropomorphic robotic arms by combining data from multiple sensors, such as cameras, data gloves, force sensors, etc. By fusing and processing this sensing information, it can enable real-time control of anthropomorphic robotic arms and dexterous hands, replicating the motion of human manipulators. In this paper, we present a sensor fusion-based teleoperation control system for the anthropomorphic robotic arm and dexterous hand, which utilizes a filter to fuse data from multiple sensors in real-time. As such, the real-time perceived human arms motion posture information is analyzed and processed, and wireless communication is used to intelligently and flexibly control the anthropomorphic robotic arm and dexterous hand. Finally, the user is able to manage the anthropomorphic operation function in a stable and reliable manner. We also discussed the implementation and experimental evaluation of the system, showing that it is able to achieve improved performance and stability compared to traditional teleoperation control methods.

Vision-Based Robot Arm Control Interface for Retrieving Objects from the Floor

Approximately half of the patients with spinal cord injuries in Japan have a cervical spinal cord injury. Owing to the trunk dysfunction, patients with high-level spinal cord injuries have particular difficulty when searching for or picking up objects from the floor. Recently, welfare robot arms have been developed to help such individuals increase self-reliance. In this study, we propose an operating system that includes an eye-in-hand system with a touchscreen interface for grasping objects from the floor and delivering them to the individual. In the proposed method, the visual information of the target object is shown on a touchscreen interface. The patient specifies the target position for the robot arm by drawing a line on the target object on the interface. We conducted an experiment to compare the proposed interface with an on-screen joystick to demonstrate the proposed system’s efficiency and its ability to reduce physical burden. The results show that the proposed method is both quicker to use and effectively reduces the physical burden on the user compared to the conventional method.

Persistent Human–Machine Interfaces for Robotic Arm Control Via Gaze and Eye Direction Tracking

Recent advances in sensors and electronics have enabled electrooculogram (EOG) detection systems for capturing eye movements. However, EOG signals are susceptible to the sensor's skin‐contact quality, limiting the precise detection of eye angles and gaze. Herein, a two‐camera eye‐tracking system and a data classification method for persistent human–machine interfaces (HMIs) are introduced. Machine‐learning technology is used for a continuous real‐time classification of gaze and eye directions, to precisely control a robotic arm. In addition, a deep‐learning algorithm for classifying eye directions is developed and the pupil center‐corneal reflection method of an eye tracker for gaze tracking is utilized. A supervisory control and data acquisition architecture that can be universally applied to any screen‐based HMI task are used by the system. It is shown in the study that the classification algorithm using deep learning enables exceptional accuracy (99.99%) with the number of actions per command (≥64), the highest performance compared to other HMI systems. Demonstrating real‐time control of a robotic arm captures the unique advantages of the precise eye‐tracking system for playing chess and manipulating dice. Overall, this paper shows the HMI system's potential for remote control of surgery robots, warehouse systems, and construction tools.

UNCER: A framework for uncertainty estimation and reduction in neural decoding of EEG signals

EEG decoding systems based on deep neural networks have been widely used in the decision-making of brain-computer interfaces (BCI). Their predictions, however, can be unreliable given the significant variance and noise in EEG signals. Previous works on EEG analysis mainly focus on the exploration of noise patterns in the source signal, while the uncertainty during the decoding process is largely unexplored. Automatically detecting and reducing such decoding uncertainty is important for BCI motor imagery applications such as robotic arm control etc. In this work, we proposed an uncertainty estimation and reduction model (UNCER) to quantify and mitigate the uncertainty during the EEG decoding process. It utilized a combination of dropout oriented method and Bayesian neural network for uncertainty estimation to incorporate both the uncertainty in the input signal and the uncertainty in the model parameters. We further proposed an adaptive data augmentation-based approach for uncertainty reduction. The model can be integrated into the current widely used EEG neural decoders without change of the architecture. We performed extensive experiments for uncertainty estimation and its reduction in both intra-subject EEG decoding and cross-subject EEG decoding on two public motor imagery datasets, where the proposed model achieves significant improvement both in the quality of estimated uncertainty and the effectiveness of uncertainty reduction.

Dynamic Control of Soft Robotic Arm: An Experimental Study

The objective of this letter is to investigate the dynamic control of a soft robotic arm. First, a modular soft robotic hardware and an affordable actuator-space encoder were presented. We then discussed the soft robot modeling, and adaptive passivity control strategy with stability proof. The proposed controller was tested in different operation scenarios, and compared to the standard PD feedback linearization control and passivity control. In all experimental settings, the adaptive passivity control scheme was able to achieve superior performance, even with significant modeling uncertainties and disturbances. The theoretical analysis and experimental validations of the proposed controller will pave the way toward the practical implementations of the soft robotic system in the dynamic scenarios.

A Mobile Robot with a Manipulator to Alleviate the Shortage of Health Workers in Hospitals in COVID 19

COVID-19 has brought an unprecedented burden to hospitals. Hospitals are facing many problems, such as understaffing and infected employees. But this has also promoted the development of hospital robots to a certain extent. This paper aimed to develop a robot that can move around the hospital and pick and send stuff to patients. The robot is mainly divided into 2 two parts: the moving part and the grasping part. The moving part includes map construction, localization, and navigation with gmapping, A*, and DWA algorithms that would stimulate on the ROS platform. For the grasping part, the forward and inverse kinematics of the robot arm and PD control would be analyzed using MATLAB with the Robotics System Toolbox, Simulink, and Simscape Multibody to simulate and control the robot arm. Using this approach, the robot can reach the target position and avoid dynamic obstacles. In addition, the robot arm can pick up and drop the ball with the designed trajectory.

Design of FPGA Based Robot Arm Controller Using Verilog HDL

This article describes the controlling of robot arm using different operations with choice. It is a programmable arm to hold and place the objects. Also it can change the position of the objects. The addition features are added to robotic arm using a reprogrammable FPGA mechanism. The robotic arm parts are jointed to give flexible move and rotate movements. We have used spartan3 FPGA to implement and test the robot arm conditions. We have used digital controlling inputs to control the robot arm directions.

A robotic arm control system with simultaneous and sequential modes combining eye-tracking with steady-state visual evoked potential in virtual reality environment.

At present, single-modal brain-computer interface (BCI) still has limitations in practical application, such as low flexibility, poor autonomy, and easy fatigue for subjects. This study developed an asynchronous robotic arm control system based on steady-state visual evoked potentials (SSVEP) and eye-tracking in virtual reality (VR) environment, including simultaneous and sequential modes. For simultaneous mode, target classification was realized by decision-level fusion of electroencephalography (EEG) and eye-gaze. The stimulus duration for each subject was non-fixed, which was determined by an adjustable window method. Subjects could autonomously control the start and stop of the system using triple blink and eye closure, respectively. For sequential mode, no calibration was conducted before operation. First, subjects' gaze area was obtained through eye-gaze, and then only few stimulus blocks began to flicker. Next, target classification was determined using EEG. Additionally, subjects could reject false triggering commands using eye closure. In this study, the system effectiveness was verified through offline experiment and online robotic-arm grasping experiment. Twenty subjects participated in offline experiment. For simultaneous mode, average ACC and ITR at the stimulus duration of 0.9 s were 90.50% and 60.02 bits/min, respectively. For sequential mode, average ACC and ITR at the stimulus duration of 1.4 s were 90.47% and 45.38 bits/min, respectively. Fifteen subjects successfully completed the online tasks of grabbing balls in both modes, and most subjects preferred the sequential mode. The proposed hybrid brain-computer interface (h-BCI) system could increase autonomy, reduce visual fatigue, meet individual needs, and improve the efficiency of the system.

An online impedance adaptation controller for decoding skill intelligence

Variable Impedance control allows robots and humans to safely and efficiently interact with unknown external environments. This tutorial introduces online impedance adaptation control (OIAC) for variable compliant joint motions in a range of control tasks: rapid (<1s) movement control (i.e., whipping to hit), arm and finger impedance quantification, multifunctional exoskeleton control, and robot-inspired human arm control hypothesis. The OIAC has been introduced as a feedback control, which can be integrated into a feedforward control, e.g., learned by data-driven methods. This integration facilitates the understanding of human and robot arm control, closing a research loop between biomechanics and robotics. It shows not only a research way from biomechanics to robotics, but also another reserved one. This tutorial aims at presenting research examples and Python codes for advancing the understanding of variable impedance adaptation in human and robot motor control. It contributes to the state-of-the-art by providing an online impedance adaptation controller for wearable robots (i.e., exoskeletons) which can be used in robotic and biomechanical applications.

Reinforcement Learning-Based Prescribed Performance Motion Control of Pneumatic Muscle Actuated Robotic Arms With Measurement Noises

Featured with high power density, excellent flexibility, shock absorption capacity, etc., pneumatic muscles (PMs) promote the development of exoskeleton robots and rehabilitation equipment. However, the complex nonlinearities of PMs limit efficiency optimization in closed-loop control, while the force-displacement coupling, soft materials, deficient workspace, etc., make it more difficult to simultaneously increase motion speeds and ensure the safety of multiple PM-actuated (PMA) robots. Although force sensors can currently be replaced by applying state estimation techniques, the amplification effects of measurement noises still compromise control accuracy and stability in practice. To this end, this article proposes a reinforcement learning-based robust motion control method with the prescribed performance, which achieves efficient and satisfactory tracking control for PMA robotic arms. In particular, by elaborately incorporating an integral term, a robust generalized proportional integral observer is used to eliminate measurement noises. Meanwhile, by using an actor–critic network to optimize control performance, an error-transformation-based continuous controller is designed to guarantee the uniformly ultimately boundedness of tracking errors. Compared with most existing methods, this article provides the first solution to restrict the entire transient and steady-state performance of PMA robotic arms, improve the noise suppression capability, and optimize the control efficiency simultaneously. Finally, complete stability analysis based on Lyapunov techniques is provided, and several groups of hardware experiments demonstrate the practicability and robustness of the proposed method.

Bioinspired smooth neuromorphic control for robotic arms

Beyond providing accurate movements, achieving smooth motion trajectories is a long-standing goal of robotics control theory for arms aiming to replicate natural human movements. Drawing inspiration from biological agents, whose reaching control networks effortlessly give rise to smooth and precise movements, can simplify these control objectives for robot arms. Neuromorphic processors, which mimic the brain’s computational principles, are an ideal platform to approximate the accuracy and smoothness of biological controllers while maximizing their energy efficiency and robustness. However, the incompatibility of conventional control methods with neuromorphic hardware limits the computational efficiency and explainability of their existing adaptations. In contrast, the neuronal subnetworks underlying smooth and accurate reaching movements are effective, minimal, and inherently compatible with neuromorphic hardware. In this work, we emulate these networks with a biologically realistic spiking neural network for motor control on neuromorphic hardware. The proposed controller incorporates experimentally-identified short-term synaptic plasticity and specialized neurons that regulate sensory feedback gain to provide smooth and accurate joint control across a wide motion range. Concurrently, it preserves the minimal complexity of its biological counterpart and is directly deployable on Intel’s neuromorphic processor. Using the joint controller as a building block and inspired by joint coordination in human arms, we scaled up this approach to control real-world robot arms. The trajectories and smooth, bell-shaped velocity profiles of the resulting motions resembled those of humans, verifying the biological relevance of the controller. Notably, the method achieved state-of-the-art control performance while decreasing the motion jerk by 19% to improve motion smoothness. Overall, this work suggests that control solutions inspired by experimentally identified neuronal architectures can provide effective, neuromorphic-controlled robots.