Control Of Robot Arm Research Articles

Design and Development of Robotic Arm Control by Human Hand

Design and Development of Robotic Arm Control by Human Hand

Architectural Proposal for Low-Cost Brain–Computer Interfaces with ROS Systems for the Control of Robotic Arms in Autonomous Wheelchairs

Neurodegenerative diseases present significant challenges in terms of mobility and autonomy for patients. In the current context of technological advances, brain–computer interfaces (BCIs) emerge as a promising tool to improve the quality of life of these patients. Therefore, in this study, we explore the feasibility of using low-cost commercial EEG headsets, such as Neurosky and Brainlink, for the control of robotic arms integrated into autonomous wheelchairs. These headbands, which offer attention and meditation values, have been adapted to provide intuitive control based on the eight EEG signal values read from Delta to Gamma (high and low/medium Gamma) collected from the users’ prefrontal area, using only two non-invasive electrodes. To ensure precise and adaptive control, we have incorporated a neural network that interprets these values in real time so that the response of the robotic arm matches the user’s intentions. The results suggest that this combination of BCIs, robotics, and machine learning techniques, such as neural networks, is not only technically feasible but also has the potential to radically transform the interaction of patients with neurodegenerative diseases with their environment.

Design of control system for six-degree-of-freedom robotic arm

To study the robotic arm trajectory planning and motion, the six-degree-of-freedom robotic arm control system design is proposed. In this paper, we choose a multi-joint type of robotic arm. We analyze the design of the robotic arm at home and abroad and determine the specific servomotor model and the specific robotic arm mechanism. We analyze the torque relationship of each joint and the size of the servomotor. Using SolidWorks and Robot Studios, we carry out the three-dimensional design modeling and simulation of the robotic arm, design the whole set of robotic arms, and use the Arduino Uno development board for precise control of the servo motor. The control program was written using C language, and various control tests were carried out on the robotic arm to make sure that the system could satisfy the debugging and research of trajectory planning type.

A Dynamic Modeling Method for Soft Pneumatic Robotic Arms Considering Both Geometric Nonlinearity and Visco-Hyperelasticity

This work proposes a new dynamic modeling approach that integrates the principle of virtual power and a spring–damper–fluid equivalent method. It is able to simultaneously consider the geometric and material nonlinearity including hyperelasticity and viscoelasticity of the soft robotic arm. Meanwhile, the nonuniform deformation of the soft arm wall can be introduced into the model based on the equivalent model. A general prototype of soft robotic arms is fabricated and validation experiments of three pneumatic actuation modes are carried out. A maximum position error of 3.64[Formula: see text]mm at the end of the soft arm is obtained with an eight-segment theoretical model for the 280[Formula: see text]mm long and 30[Formula: see text]mm thick prototype in an approximate step actuation test with a maximum pneumatic pressure of 11.5[Formula: see text]kPa. The comparisons between the theoretical prediction and experimental results demonstrate a high accuracy of the proposed modeling method. Besides, simulations of dynamic motions under in-plane and out-of-plane actuation are carried out respectively, illustrating that the proposed method has the ability to describe a variety of actuation forms. The proposed dynamic modeling method provides a new way for modeling the soft robotic arms and has guiding significance for the design and control of soft robotic arms.

Utilizing Inverse Kinematics for Precise Guidance in Planning 6-DoF Robot End-Effector Movements

The solution of the kinematic inverse determines a substantial part of the robotic arm's control accuracy. Researchers frequently employ standard problem-solving techniques such as numerical, algebraic, iterative, and geometric methods. Although geometric like trigonometrical method has been widely studied, and their application is strongly dependent on the shape and dimensions of the robot. The complexity of the steps makes this approach difficult for researchers. In order to give a clearance and easiness, the step-by-step features of inverse kinematics are described in this research. The study begins with forward kinematics and refers to the DH-parameter in Homogeneous Matrix Transformations calculation. The existence of specific elements applied to mathematical derivation constituted the basis of forward kinematic discussions. And based on geometrical analysis, the inverse kinematic is then derived. Furthermore, simulations are performed to demonstrate the actual implementation of IK and the solution is then used to initiate the path planning process.

An design of robotic arm control algorithm for explosive disposal robots

Automatic explosive disposal robot have great potential to replace manual Explosive ordnance disposal(EOD) robots. In order to face the practical application scenarios of complex transformations, it is of great practical significance to study a fully automatic and autonomous EOD robot that can transfer dangerous goods to a safe area. In this paper, a mechanical arm algorithm based on the inverse kinematics of robot is presented. The relative position of the bomb is input by the electromagnetic induction device to guide the mechanical arm to the corresponding position. After obtaining the bomb, the deviation angle of the mechanical arm is input in real time by the gyroscope to achieve the relative stability of the object transportation. The experimental results indicate that the explosive disposal robot has certain practical significance.

Research on Multi-Hole Localization Tracking Based on a Combination of Machine Vision and Deep Learning

In the process of industrial production, manual assembly of workpieces exists with low efficiency and high intensity, and some of the assembly process of the human body has a certain degree of danger. At the same time, traditional machine learning algorithms are difficult to adapt to the complexity of the current industrial field environment; the change in the environment will greatly affect the accuracy of the robot’s work. Therefore, this paper proposes a method based on the combination of machine vision and the YOLOv5 deep learning model to obtain the disk porous localization information, after coordinate mapping by the ROS communication control robotic arm work, in order to improve the anti-interference ability of the environment and work efficiency but also reduce the danger to the human body. The system utilizes a camera to collect real-time images of targets in complex environments and, then, trains and processes them for recognition such that coordinate localization information can be obtained. This information is converted into coordinates under the robot coordinate system through hand–eye calibration, and the robot is then controlled to complete multi-hole localization and tracking by means of communication between the upper and lower computers. The results show that there is a high accuracy in the training and testing of the target object, and the control accuracy of the robotic arm is also relatively high. The method has strong anti-interference to the complex environment of industry and exhibits a certain feasibility and effectiveness. It lays a foundation for achieving the automated installation of docking disk workpieces in industrial production and also provides a more favorable choice for the production and installation of the process of screw positioning needs.

A Novel Robotic Controller Using Neural Engineering Framework-Based Spiking Neural Networks.

This paper investigates spiking neural networks (SNN) for novel robotic controllers with the aim of improving accuracy in trajectory tracking. By emulating the operation of the human brain through the incorporation of temporal coding mechanisms, SNN offer greater adaptability and efficiency in information processing, providing significant advantages in the representation of temporal information in robotic arm control compared to conventional neural networks. Exploring specific implementations of SNN in robot control, this study analyzes neuron models and learning mechanisms inherent to SNN. Based on the principles of the Neural Engineering Framework (NEF), a novel spiking PID controller is designed and simulated for a 3-DoF robotic arm using Nengo and MATLAB R2022b. The controller demonstrated good accuracy and efficiency in following designated trajectories, showing minimal deviations, overshoots, or oscillations. A thorough quantitative assessment, utilizing performance metrics like root mean square error (RMSE) and the integral of the absolute value of the time-weighted error (ITAE), provides additional validation for the efficacy of the SNN-based controller. Competitive performance was observed, surpassing a fuzzy controller by 5% in terms of the ITAE index and a conventional PID controller by 6% in the ITAE index and 30% in RMSE performance. This work highlights the utility of NEF and SNN in developing effective robotic controllers, laying the groundwork for future research focused on SNN adaptability in dynamic environments and advanced robotic applications.

Identification Localization and Gathering of Pepper Based on Depth Camera and Inverse Kinematics

Highlights The spatial coordinate position of peppers is obtained by YOLOv5 algorithm fused with localization technique. The inverse kinematics for gathering arm is solved by disassembly and Euler-angle method. Pentadic polynomials are applied to realize the motion planning and control of robotic arm. The proposed method achieves an average gathering success rate about 60%. Abstract. To avoid mutation and interference with intelligent gathering, an autonomous gathering method is proposed in this paper. An improved YOLOv5 target detection algorithm is used to recognize peppers. The kinematics solution of the robotic arm is realized by using the disassembly method and the Euler-angle method. Simulating and modeling robotic arms is conducted by the Robotic Toolbox in Matlab. The robotic arm's spatial motion planning is controlled by the pentadic polynomial interpolation method. The feasibility of the inverse solution and planning is verified in Matlab. Different conditions’ gathering experiments are conducted on the gathering platform. Experiments show that this method is smooth and interference-free, with a high gathering success rate, realizing the accurate gathering of single, small fruits in complex environments. Keywords: Robot gathering, Robotic arm inverse kinematics, Target detection, Trajectory planning

Development of an EOD Robot for the Arequipa Explosive Disposal Unit

The JVC 0.2 is an Explosive Ordnance Disposal (EOD) robot with tracks, a 5 Degrees of Freedom (DOF) arm, and a two-finger gripper developed by the National University of San Agustin (UNSA) and the Explosive Ordnance Disposal Unit of the Police of Arequipa (UDEX) in response to the technical needs of adequate weight for manual transport in compliance with the law 29,088 in force in Peru, urban multi-terrain locomotion skills, robotic arm control and handling of explosive objects with sizes and weights registered in the UDEX database. The objective of this document is to develop and test the robot for maneuverability, locomotion, and arm strength based on National Institute of Standards and Technology (NIST) standards adapted to the needs of UDEX to safeguard the lives of Explosive Ordnance Disposal Specialists (TEDAX). The test procedures were carried out to evaluate the robot’s capabilities in terms of maneuverability and were designed to time the transport of objects simulating explosives at different inclinations to a container. The tests showed us that the actuation time of the robotic arm was improved, that it can climb stairs with a maximum inclination of 20°, that the locomotion speed measured on flat ground was 10.94 cm/s and that the load capacity at the extended arm is 9 kg. Finally, we conclude that it is necessary to update the design of the robot to reduce the weight to meet the safety standards at work, increase the load capacity up to 10 Kg, and improve mobility; so that, it becomes of daily use by the TEDAX.

IoT-based remote control for robotic arm

IoT-based remote control for robotic arm

IoT-based remote control for robotic arm

IoT-based remote control for robotic arm

SoftSling: A Soft Robotic Arm Control Strategy to Throw Objects with Circular Run-ups

SoftSling: A Soft Robotic Arm Control Strategy to Throw Objects with Circular Run-ups

Dizajn i razvoj troškovno-efikasne robotske ruke sa robotskim kontrolerom baziranim na PLC-u

To develop a cost-efficient robot arm for a typical pick and place application that can applied in industry, this paper deployed a programmable logic controller (PLC) to control the rotation motion of the robot joints. The main tasks of the PLC controller are to calculate the kinematics, create high-speed pulse outputs for stepper motors, and implement sequence operations for a certain application. Functions are written into subprogram segments. When needed, the main program only turns on the corresponding flag for executing the subprogram. Using the pre-written subprograms, a logical sequence to implement the Pick and Place application is easily implemented and described in this paper. The PLC program is developed to control a SCARA robot with three rotation joins. Stepper motors drive the robot joints. The Delta DVPSV2 PLC is utilized to design the robot controller. This PLC series has four high-speed pulse output pins, which is suitable for this project. Synchronous motion of stepper motors is easily performed using high-speed pulse output commands built into the PLC program. Experimental results of robot arm control have demonstrated the efficiency and accuracy of the developed program. The robot arm's forward and inverse kinematics problems are verified using the simulator on the software. The robot's joints move synchronously as required to perform pick-and-place applications.

Iteratively Calibratable Network for Reliable EEG-Based Robotic Arm Control.

Robotic arms are increasingly being utilized in shared workspaces, which necessitates the accurate interpretation of human intentions for both efficiency and safety. Electroencephalogram (EEG) signals, commonly employed to measure brain activity, offer a direct communication channel between humans and robotic arms. However, the ambiguous and unstable characteristics of EEG signals, coupled with their widespread distribution, make it challenging to collect sufficient data and hinder the calibration performance for new signals, thereby reducing the reliability of EEG-based applications. To address these issues, this study proposes an iteratively calibratable network aimed at enhancing the reliability and efficiency of EEG-based robotic arm control systems. The proposed method integrates feature inputs with network expansion techniques. This integration allows a network trained on an extensive initial dataset to adapt effectively to new users during calibration. Additionally, our approach combines motor imagery and speech imagery datasets to increase not only its intuitiveness but also the number of command classes. The evaluation is conducted in a pseudo-online manner, with a robotic arm operating in real-time to collect data, which is then analyzed offline. The evaluation results demonstrated that the proposed method outperformed the comparison group in 10 sessions and demonstrated competitive results when the two paradigms were combined. Therefore, it was confirmed that the network can be calibrated and personalized using only the new data from new users.

IoST-Enabled Robotic Arm Control and Abnormality Prediction Using Minimal Flex Sensors and Gaussian Mixture Models

IoST-Enabled Robotic Arm Control and Abnormality Prediction Using Minimal Flex Sensors and Gaussian Mixture Models

Robotic arm control system based on triple closed-loop BLDC

Robotic arm control system based on triple closed-loop BLDC

Application of artificial bee colony algorithm as numerical solution for first order IVPs and industrial robot arm control problem

Application of artificial bee colony algorithm as numerical solution for first order IVPs and industrial robot arm control problem

Using Goal-Conditioned Reinforcement Learning With Deep Imitation to Control Robot Arm in Flexible Flat Cable Assembly Task

Using Goal-Conditioned Reinforcement Learning With Deep Imitation to Control Robot Arm in Flexible Flat Cable Assembly Task

Brian Electrical Activity Acquisition and Decoding Algorithms For Using BMIs

Physical disabilities in limbs often lead to severe impacts on personal life and work. However, BMI technology offers hope for amputees or paralyzed individuals to regain control and motor abilities. By reading signals from the brain's motor cortex, BMI technology enables the control of robotic arms, cursors, and more, facilitating functional recovery. The key lies in identifying and extracting information related to specific tasks or intentions from brain signals, where the decoder determines the accuracy, reliability, and sensitivity of the final movements. This paper aims to provide a brief overview of the decoding steps in BMIs. The paper will discuss how the raw signals are acquired from the brain, including invasive BMI and noninvasive BMI, along with the respective advantages and disadvantages of these two approaches. Then, it will explore how these signals are processed into specific commands through both linear and nonlinear algorithms. Additionally, the paper will discuss the strengths, limitations, and feasibility of different decoding methods in human-computer interaction applications. This review offers researchers a preliminary understanding of the acquisition and decoding algorithms for brain electrical activity in BMIs. By evaluating the strengths and limitations of different algorithms, we can provide guidance for designing and improving BMI systems, thereby supporting the achievement of more accurate and efficient human-computer interaction.