DOF Robotic Arm Research Articles

Integrating artificial immune systems and multi-layer perceptron-biogeography-based optimization for enhanced inverse kinematics in robotic arm

Determining required joint angles to achieve a desired position in a manipulator’s arm is a complicated problem without simple analytical solutions. This paper researches several computational methods based on artificial intelligence (AI) for calculating the joint positions of the 6-DOF robotic arm. We can extrapolate relevance, for example, to the crucial role that robotic manipulator arms play in industrial and medical applications, where enhanced precision and movement efficiency may sharply boost performance and expand applicability. Here, we investigate the effectiveness of methods, such as the artificial immune system (AIS) and multi-layer perceptron-biogeography-based optimization (MLP-BBO). Those AI-driven methods have been applied to determine joint angles for reaching desired positions through simulations for the robotic arm. The results show that the AIS and MLP-BBO approach can handle the intrinsic complexities of the task, thus testifying to the practicability and dependability of these two methods in this application. From the findings in the study, it was indicated that AI-driven techniques can effectively answer the complex problem of the robotic manipulator arm in finding joint angles.

RAVEN: development of a novel robotic additive manufacturing system towards small-scale fabrication of complex scaffolds for regenerative medicine

ABSTRACT Recent technological advances in the field of Additive Manufacturing (AM) and the increasing need in Regenerative Medicine (RM) for devices that better and better mimic native tissue architecture are showing limitations in the current scaffolds fabrication techniques. A switch from the typical layer-by-layer approach is needed to achieve precise control on fibres orientation and pores dimension and morphology. In this work a new AM apparatus, the RAVEN (Robot-Assisted Volumetric ExtrusioN) system, is presented. RAVEN is based on a 7-DOF robotic arm and an extruder and allows for volumetric extrusion of polymeric filaments. The development process, namely the robotic motion optimisation, the optimisation towards small-scale trajectories, the custom-made hardware/software interfaces, and the different printing capabilities are hereby presented. The successful results are promising towards future advanced applications in which the ability of the robot to change its configuration while printing will be crucial.

Design and Construction of 5-Dof EMG Based Robotic Arm System

Electromyography (EMG) is an alternate method of obtaining muscle signal outputs. As a result, the current development in designing my electric plans has piqued the attention of researchers in this subject. This is because Standard controllers lack essential components, limiting the use of limbs to operate equipment, namely an arm controlled by robotics. EMG signals are subject to noise, including crosstalk, motion artifacts, ambient noise, and intrinsic noise. Electromyography preparation requires careful selection of muscle groups, electrode placement, and environment quality, all of which impact the signal output. The goal of this study is to create an EMG-based robotic arm control system that can be used to help the elderly, individuals with impairments, and those who operate in dangerous places. Initially, a literature review on current human-robot interaction approaches and analysis of the kinematics of a 5 DOF robotic arm has been conducted. Then, utilizing Electromyogram (EMG) data obtained from the muscles of the elbow, away for controlling a 5 DOF robotic arm is provided. Two accelerometers are utilized to record the human arm's gesture and posture, this information is then communicated to the robotic arm as an input. In the experiments, wrist rotation, left, right, up, and down movements were used to establish controlled motion of a 5 DOF robotic arm. The project's goal has been met with the successful development of an EMG-controlled robotic arm. The robotic arm may yet be improved by including Implementing a wireless sensor network with multiple channels.

Synergistic Integration of AI and 6-DOF Robotic Arm for Enhanced Bomb Disposal Capabilities

Synergistic Integration of AI and 6-DOF Robotic Arm for Enhanced Bomb Disposal Capabilities

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

Analysis of Polynomial Interpolation Characteristics Based on 6DOF Manipulator Trajectory Planning

Aiming at the problems of choosing and using polynomials of different times and mixed polynomials in the process of manipulator trajectory planning, such as difficulty, wide range of light and low accuracy, in this paper, the application of different polynomials in manipulator trajectory planning is systematically analyzed by using MATLAB Robot Toolbox simulation platform and Polynomial interpolation algorithm. Using a 6-dof robotic arm Puma560 as a simulation object, the effects of three-, five-, and seven-Polynomial interpolation on joint position and pose in joint space trajectory planning were analyzed, a preliminary understanding of the application of different polynomials, and secondly, the more complex application of the fifth and seventh degree polynomials in Descartes space for the trajectory planning of the manipulator, the influence of trajectory planning with different degree polynomials on the position and pose of manipulator joint is clarified. The results show that the higher the number of polynomials, the smoother the transition of the joint at the critical path points, but the greater the maximum angular velocity and angular acceleration of the joint, the Polynomial interpolation of joint space and Descartes space trajectory planning are 14% and 23% lower than the average of absolute maximum angular velocity and absolute maximum angular acceleration of the Polynomial interpolation, respectively. Based on the analysis of the research results, this paper presents the applicable conditions and applications of Polynomial interpolation method in trajectory planning, and further summarizes the application conditions and methods of mixed polynomials.

Designing and Implementing a 3DOF Robotic Arm for Color Sorting and Object Identification Using Computer Vision Technology

This paper focuses on designing a three-degree-of-freedom robotic arm for color sorting using computer vision technology. It involves the use of a robotic arm composed of three joints and a gripper for grasping and manipulating objects. Each joint is actuated by a servo motor that is controlled by an Arduino microcontroller to control the angles of the joints and achieve the desired motion. The microcontroller relies on a camera to detect and analyze colors. The camera captures an image of the objects present in the designated area and converts it into digital data to be processed later by a computer. Then, the well-known (OpenCV) and (NumPy) library in the Python programming language is used to process this data and identify the colors. Once the color is determined, the corresponding Python command is executed through the Arduino microcontroller. That allows the robotic arm to move towards the designated location based on the identified color. Consequently, the arm grasps the detected object and places it in the appropriate position according to its color. The effectiveness of the proposed approach has been verified in real-time experiments.

Design and Implementation of a 3 Degrees of Freedom Robotic Arm Powered by Pneumatic Artificial Muscle

The development of a 3-DOF robotic arm powered by pneumatic artificial muscles is represented as an innovative approach in the field of robotics, with the advantages of lightweight and flexible design being combined with the power and control benefits of pneumatic actuation. The design process, challenges, and solutions that were encountered in the development of the robotic arm are outlined in this paper, with an emphasis on its potential applications in industrial and research settings being highlighted. Additionally, the robotic arm’s modular design is enabled to allow easy customization and scalability, making it possible for the arm to be tailored to a wide range of tasks, from precise laboratory work to more robust industrial applications.

Discrete-Time Visual Servoing Control with Adaptive Image Feature Prediction Based on Manipulator Dynamics.

In this paper, a practical discrete-time control method with adaptive image feature prediction for the image-based visual servoing (IBVS) scheme is presented. In the discrete-time IBVS inner-loop/outer-loop control architecture, the time delay caused by image capture and computation is noticed. Considering the dynamic characteristics of a 6-DOF manipulator velocity input system, we propose a linear dynamic model to describe the motion of a robot end effector. Furthermore, for better estimation of image features and smoothing of the robot's velocity input, we propose an adaptive image feature prediction method that employs past image feature data and real robot velocity data to adopt the prediction parameters. The experimental results on a 6-DOF robotic arm demonstrate that the proposed method can ensure system stability and accelerate system convergence.

Path planning and inspection of complex-shaped 3D-printed composites based on robotic pulse-echo laser ultrasonic testing

With the recent advancement in manufacturing, there has been an increasing demand for composite workpieces in the aviation industry. The emergence of composite 3D printing technology that enables the simplification and automation of composite manufacturing processes presents the possibility of replacing the manufacturing process of secondary structures in an unmanned aerial vehicle (UAV). To evaluate the applicability of the technology, a solution for automated non-destructive testing (NDT) that can be applied after the 3D printing process is needed. This study proposes an advanced approach to utilizing laser ultrasonic testing (LUT) for complex-shaped structures fabricated through continuous fiber 3D printing. To ensure accurate defect detection and localization, which are crucial factors in NDT, we propose a method that utilizes a six-degree-of-freedom (6-DOF) robot arm to control the position of the inspection target. By calculating the scan points and path through coordinate transformation and spatial rotation using a quaternion, all surfaces of the targets were inspected. This maintains a constant stand-off distance (SOD) while the sensing laser was vertically incident on the target surface, resulting in a high signal-to-noise ratio (SNR) of the ultrasonic signals. Inspection results have demonstrated the proposed technique to be suitable for detecting defects in complex-shaped 3D-printed structures.

Research on Six-Degree-of-Freedom Refueling Robotic Arm Positioning and Docking Based on RGB-D Visual Guidance

The main contribution of this paper is the proposal of a six-degree-of-freedom (6-DoF) refueling robotic arm positioning and docking technology guided by RGB-D camera visual guidance, as well as conducting in-depth research and experimental validation on the technology. We have integrated the YOLOv8 algorithm with the Perspective-n-Point (PnP) algorithm to achieve precise detection and pose estimation of the target refueling interface. The focus is on resolving the recognition and positioning challenges of a specialized refueling interface by the 6-DoF robotic arm during the automated refueling process. To capture the unique characteristics of the refueling interface, we developed a dedicated dataset for the specialized refueling connectors, ensuring the YOLO algorithm’s accurate identification of the target interfaces. Subsequently, the detected interface information is converted into precise 6-DoF pose data using the PnP algorithm. These data are used to determine the desired end-effector pose of the robotic arm. The robotic arm’s movements are controlled through a trajectory planning algorithm to complete the refueling gun docking process. An experimental setup was established in the laboratory to validate the accuracy of the visual recognition and the applicability of the robotic arm’s docking posture. The experimental results demonstrate that under general lighting conditions, the recognition accuracy of this docking interface method meets the docking requirements. Compared to traditional vision-guided methods based on OpenCV, this visual guidance algorithm exhibits better adaptability and effectively provides pose information for the robotic arm.

Cost-effective Robotic Arm Simulation and System Verification

In recent years, the utilization of virtual environments in industry 4.0 has witnessed significant growth, particularly in the design, implementation, and management of robotic systems. This paper addresses the need for enhanced control in robotic arms by presenting the design and implementation of a 5DoF robotic arm transformed into a digital platform through specialized software. The methods employed involve detailed direct and inverse kinematic modeling to replicate the physical arm in a digital environment. Our measurements indicate an impressive accuracy ranging from 97% to 100% in the movements of the digital model, closely mirroring its physical counterpart. This research not only contributes to the development of simulation systems but also holds promise for the broader adoption of digital twins. The paper discusses the background, outlines the methodology, highlights key findings, and concludes with the potential future impact of this work on the advancement of robotic systems and simulation technologies.

Exploring Saliency for Learning Sensory-Motor Contingencies in Loco-Manipulation Tasks

The objective of this paper is to propose a framework for a robot to learn multiple Sensory-Motor Contingencies from human demonstrations and reproduce them. Sensory-Motor Contingencies are a concept that describes intelligent behavior of animals and humans in relation to their environment. They have been used to design control and planning algorithms for robots capable of interacting and adapting autonomously. However, enabling a robot to autonomously develop Sensory-Motor Contingencies is challenging due to the complexity of action and perception signals. This framework leverages tools from Learning from Demonstrations to have the robot memorize various sensory phases and corresponding motor actions through an attention mechanism. This generates a metric in the perception space, used by the robot to determine which sensory-motor memory is contingent to the current context. The robot generalizes the memorized actions to adapt them to the present perception. This process creates a discrete lattice of continuous Sensory-Motor Contingencies that can control a robot in loco-manipulation tasks. Experiments on a 7-dof collaborative robotic arm with a gripper, and on a mobile manipulator demonstrate the functionality and versatility of the framework.

Anomaly Detection of Tire Tiny Text: Mechanism and Method

Due to the variety of tire specifications and poor contrast, it is difficult for ordinary 2D vision-based methods to accurately and autonomously detect anomalies in the tire text embossed on the tire sidewalls. In this paper, a 3D vision-based autonomous scanning mechanism and method is proposed, including three core components: a three-degree-of-freedom (3-DoF) robotic arm, a high-precision turntable and a composite vision probe. Based on the designed vision probe, the mechanism can autonomously scan the tire sidewall through a series of optimal viewpoints to obtain high-quality tire images even in the absence of tire CAD models. To obtain the optimal viewpoints, an information-driven viewpoint planning method is proposed. Combined with the real-time viewpoint adjustment strategy, the viewpoint of the vision probe is dynamically fine-tuned, which can effectively balance the scanning precision and motion cost. In the text anomaly detection phase, a three-stage tire text anomaly detection method based on deep learning and statistics strategy is proposed, which can accurately detect and recognize tire text, identify sub-mold categories, and judge text anomalies. Experimental results show that the proposed mechanism and method perform well in terms of accuracy ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$&gt;$</tex-math> </inline-formula> 96%) and efficiency (time per lap for a tire is less than 9 s); it shows that the proposed mechanism and method have certain industrial application value and can be applied to tire quality inspection of on-line production. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —Tire text may be incorrectly embossed during tire manufacturing. Due to the wide variety of tire specifications, poor contrast, and thin text strokes, it is difficult to detect abnormal tire text. In this paper, a 3D vision-based mechanism and method is developed to address this issue. Using a specially designed composite vision probe and scanning method, the mechanism can observe tiny text information on tires of different specifications from multiple angles and directions. By using our tire text anomaly detection method based deep learning and statistics strategy, the mechanism can accurately and robustly detect the region and content of abnormal text. The mechanism is efficient (with a detection time of less than 9 s per lap for one tire) and accurate ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$&gt;$</tex-math> </inline-formula> 96%), indicating that it has great potential in the first inspection or quality inspection step of tire manufacturing in industrial scenarios.

Assistance Device Based on SSVEP-BCI Online to Control a 6-DOF Robotic Arm.

This paper explores the potential benefits of integrating a brain-computer interface (BCI) utilizing the visual-evoked potential paradigm (SSVEP) with a six-degrees-of-freedom (6-DOF) robotic arm to enhance rehabilitation tools. The SSVEP-BCI employs electroencephalography (EEG) as a method of measuring neural responses inside the occipital lobe in reaction to pre-established visual stimulus frequencies. The BCI offline and online studies yielded accuracy rates of 75% and 83%, respectively, indicating the efficacy of the system in accurately detecting and capturing user intent. The robotic arm achieves planar motion by utilizing a total of five control frequencies. The results of this experiment exhibited a high level of precision and consistency, as indicated by the recorded values of ±0.85 and ±1.49 cm for accuracy and repeatability, respectively. Moreover, during the performance tests conducted with the task of constructing a square within each plane, the system demonstrated accuracy of 79% and 83%. The use of SSVEP-BCI and a robotic arm together shows promise and sets a solid foundation for the development of assistive technologies that aim to improve the health of people with amyotrophic lateral sclerosis, spina bifida, and other related diseases.

Robotic Arm Vehicle for Planting

Abstract: The advancement in Robotics technology has opened up new possibilities in many fields such as manufacturing, medical, industrial and defence. As agriculture labour becomes increasingly costly and scarce, we can displace workers with robotics technology, reducing the financial burden of small farmers. This paper aims to develop an android-controlled 6 DOF robotic arm and drill mechanism attached to a vehicle for planting a sapling. Through a drill mechanism, holes are made on one side and planting is done by a robotic arm on the other side. This prototype will be controlled using an Android application connected to the Arduino board via Bluetooth. Robotic arms are capable of performing a task repetitively.

RRR Robotik Kol için Çok Katmanlı Algılayıcı ve Ters Kinematik Karşılaştırması

In this study, the position control simulation of a 3 Degree of Freedom (3DOF) robot arm was compared with machine learning and inverse kinematic analysis separately. The considered robot arm is designed in RRR pattern. In the inverse kinematic analysis of the robot arm, the geometric approach and the analytical approach are used together. Multi-Layer Perceptron (MLP) was used as a machine learning method. Some of the coordinate data that the robot arm can reach in the working space are selected and the MLP model is trained with these data. When training was done with MLP machine learning method, the correlation coefficient (R2) was obtained as 1. Coordinates of 3 different geometric models (helix, star and daisy) that can be included in the working space are used as test data of the MLP model. These tests are simulated in 3D in MATLAB environment. The simulation results were compared with the inverse kinematics analysis data. As a result, Mean Relative Error (MRE) values for helix, star and daisy shapes were calculated as 0.0007, 0.0033 and 0.0011, respectively, in the tests performed. Mean Squared Error (MSE) values were obtained as 0.0034, 0.0065 and 0.0040, respectively. This confirms that the proposed MLP model can operate this system at the desired stability.

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.

Multitask control of aerial manipulator robots with dynamic compensation based on numerical methods

This paper presents a control scheme for aerial manipulators which allows to solve different motion problems: end-effector position control, end-effector trajectory tracking control and path-following control. The scheme has two cascaded controllers: i) the first controller is a minimum norm controller based on numerical methods, it solves the three motion control problems just by modifying the controller references. Also, since the aerial manipulator robot is a redundant system, i.e., it has extra degrees of freedom to accomplish the task, it is possible to set other control objectives in a hierarchical order. As a secondary objective of the control it is proposed to maintain a desired configuration for the robotic arm during the task. ii) The second cascade controller is designed to compensate the dynamics of the system which main objective is to drive the velocity errors to zero. The coupled dynamic model of the robotic system (hexarotor and robotic arm) is presented. This model is usually developed as a function of the forces and torques. However, in this work, it is written as a function of reference velocities which are usual references for these vehicles. The proposed control algorithms are given with the corresponding stability and robustness analysis. Finally, to validate the control scheme, experimental tests are performed in a partially structured environment with an aerial manipulator conformed by an aerial platform and a 3DOF robotic arm.

Dual Performance Optimization of 6-DOF Robotic Arm Trajectories in Biomedical Applications

For the first time, dual-performance perfection technologies were used to kinematically operate sophisticated robots. In this study, the trajectory development of a robot arm is optimized using a dual-performance perfection technique. The proposed approach alters the robot arm's Kinematics by creating virtual points even if the robotic system is not redundant to make it kinematically suitable for biomedical applications. In the suggested method, an appropriate objective function is chosen to raise one or maybe more performance measures while lowering one or more kinematic characteristics of a robot arm. The robot arm's end effector is set in place at the crucial locations, and the dual performance precision algorithm changes the joints and virtual points due to the robot arm's self-motion. As a result, the ideal values for the virtual points are established, and the robot arm's design is changed. Accordingly, this method's ability to visualize modifications made to the processor's design during the optimization problem is one of its benefits. The active robotic arm is used as a case study in this article. The task is defined as choosing the best path based on the input target's position and direction and is used in X-ray robot systems. The outcomes demonstrate the viability of the suggested approach and can serve as a useful prototype for an intelligent X-ray robot.