Industrial Robot Arm Research Articles

Electric Vehicle Battery Disassembly Using Interfacing Toolbox for Robotic Arms

This paper showcases the integration of the Interfacing Toolbox for Robotic Arms (ITRA) with our newly developed hybrid Visual Servoing (VS) methods to automate the disassembly of electric vehicle batteries, thereby advancing sustainability and fostering a circular economy. ITRA enhances collaboration between industrial robotic arms, server computers, sensors, and actuators, meeting the intricate demands of robotic disassembly, including the essential real-time tracking of components and robotic arms. We demonstrate the effectiveness of our hybrid VS approach, combined with ITRA, in the context of Electric Vehicle (EV) battery disassembly across two robotic testbeds. The first employs a KUKA KR10 robot for precision tasks, while the second utilizes a KUKA KR500 for operations needing higher payload capacity. Conducted in T1 (Manual Reduced Velocity) mode, our experiments underscore a swift communication protocol that links low-level and high-level control systems, thus enabling rapid object detection and tracking. This allows for the efficient completion of disassembly tasks, such as removing the EV battery’s top case in 27 s and disassembling a stack of modules in 32 s. The demonstrated success of our framework highlights its extensive applicability in robotic manufacturing sectors that demand precision and adaptability, including medical robotics, extreme environments, aerospace, and construction.

Design and Development of a Prototype of Industrial Robotic Arm Controlled by Touch Interface

Design and Development of a Prototype of Industrial Robotic Arm Controlled by Touch Interface

Continuous tow shearing for the automated manufacture of defect-free complex 3D geometry composite parts

It has always been challenging to manufacture a composite structure with complex geometry using automated fibre placement (AFP) process. The tool surface is difficult to be tessellated using fibre tapes with a finite width, without producing gaps and overlaps, and fibre steering along non-geodesic fibre paths produces various defects such as tape buckling and pull-up. In this work, a defect-free fibre-steering process on a complex surface was demonstrated by realising the continuous tow shearing (CTS) process in three dimensions. A new head control algorithm was developed, which defines the head orientations and trajectories based on the pin-jointed net model to manipulate the fibre tow using both in-plane shear and out-of-plane twisting deformations. Fibre steering process utilising this new algorithm was tested on a doubly-curved surface, using an industrial robot arm equipped with a CTS prototype head, and its layup quality and accuracy were assessed by using a three-dimensional laser profile scanner. An experimental comparison with the conventional AFP process showed that the new control algorithm enables defect-free fibre steering on complex 3D surfaces allowing for simplifying the design and analysis of novel composite structures.

Experimental analysis of augmented reality interfaces for robot programming by demonstration in manufacturing

Augmented Reality (AR) technology has been effectively utilized to support various manual operations in the manufacturing industry. An important application is serving as a user interface for human-robot collaboration. This paper presents an experimental study on the feasibility of robot programming by demonstration (PbD) through AR interfaces in the context of manufacturing. Our focus is on comparing the pointing and line tracing processes using three input methods provided by an AR headset: hand ray, head gaze, and eye gaze, based on both objective and subjective measures obtained from the experiment. The hand ray method performs the best in terms of accuracy, precision, and completion time in most experimental conditions. The SUS and NASA-TLX scores indicate acceptable usability for the hand ray method but low usability for the others. A prototyping AR tool using the hand ray as non-contact input in the real world is developed for motion planning of an industrial robotic arm. A test case of tire mold welding verifies the feasibility of the AR tool while also showing its limited capability in precision manufacturing. This work demonstrates a new approach for robot PbD on tangible objects enabled by AR.

A Novel Machine Learning System for Industrial Robot Arm Calibration

A Novel Machine Learning System for Industrial Robot Arm Calibration

Design, Analysis and Fabrication of Pick and Place Robotic Arm with Multipurpose

Abstract: This project is to design, develop and fabricate a "pick and place robotic arm" using Raspberry pi controller. It will pick and place an object from source to destination safely. This robot has a gripping technology that is used to lift, hold, grasp, and replace materials from one place to another. This robotic has revolute joints that allow the angular movement of the adjacent joint. The objective of this project is to design and build a more compact, usable and cheaper pick and place robotic arm for educational purpose. It uses Raspberry pi as the control system to control all the activities. Inputs to the Robotic arm are predefined. In the project we are applying this concept to the design of pick and place robotics system using SolidWorks Soft motion software. The software is used to design a Cartesian robot and an articulated industrial robotic arm with different grippers. The robot was designed using the SolidWorks’ 3D CAD software to shorten the robot development time, and improve the speed and quality of the robot design.

An Automatic Implementation of Oropharyngeal Swab Sampling for Diagnosing Respiratory Infectious Diseases via Soft Robotic End-Effectors

The most widely adopted method for diagnosing respiratory infectious diseases is to conduct polymerase chain reaction (PCR) assays on patients’ respiratory specimens, which are collected through either nasal or oropharyngeal swabs. The manual swab sampling process poses a high risk to the examiner and may cause false-negative results owing to improper sampling. In this paper, we propose a pneumatically actuated soft end-effector specifically designed to achieve all of the tasks involved in swab sampling. The soft end-effector utilizes circumferential instability to ensure grasping stability, and exhibits several key properties, including high load-to-weight ratio, error tolerance, and variable swab-tip stiffness, leading to successful automatic robotic oropharyngeal swab sampling, from loosening and tightening the transport medium tube cap, holding the swab, and conducting sampling, to snapping off the swab tail and sterilizing itself. Using an industrial collaborative robotic arm, we integrated the soft end-effector, force sensor, camera, lights, and remote-control stick, and developed a robotic oropharyngeal swab sampling system. Using this swab sampling system, we conducted oropharyngeal swab-sampling tests on 20 volunteers. Our Digital PCR assay results (RNase P RNA gene absolute copy numbers for the samples) revealed that our system successfully collected sufficient numbers of cells from the pharyngeal wall for respiratory disease diagnosis. In summary, we have developed a pharyngeal swab-sampling system based on an “enveloping” soft actuator, studied the sampling process, and implemented whole-process robotic oropharyngeal swab-sampling.

Magnetorheological variable stiffness and damping flexible joint with cam working surface for industrial robot

In order to improve the environmental adaptability and interaction stability of industrial robotic arms, a flexible joint with variable damping and stiffness based on magnetorheological (MR) technology was proposed. The mechanical models for variable stiffness and damping of MR flexible joint were established, and the relationship between cam surface and variable stiffness element was derived based on the energy method. Subsequently, the variation patterns of equivalent stiffness and damping of the prototype under different excitations were experimentally tested, and the test results show that its mechanical properties meet the design requirements. To further verify the performance of the MR flexible joint on the robotic arm, we setup a collision buffering test system and investigated the buffering performance of the system under different working conditions. The buffering test results indicate that when the current of variable stiffness element is 2.0 A, the peak collision acceleration of the system after installing the MR flexible joint decreases by about 40.19% compare with the rigid collision.

The Impact of Simplifications of the Dynamic Model on the Motion of a Six-Jointed Industrial Articulated Robotic Arm Movement

This research investigates the impact of model simplification on the dynamic performance of an ABB IRB-140 six-jointed industrial robotic arm, concentrating on torque prediction and energy consumption. The entire mathematical model of forward, reverse, differential kinematics, and dynamic model proposed based on the technical specifications of the arm, and to obtain the center of the mass and inertia matrices, which are essential components of the dynamic model, Utilizing Solidworks, we developed three CAD/CAM models representing the manipulator with varying detail levels, such as simplified, semi-detailed, and detailed. Our findings indicate minor differences in the model's torque and energy consumption graphs. The semi-detailed model consumed the most energy, except for joint 1, with the detailed model showing a 0.53% reduction and the simplified model a 6.8% reduction in energy consumption. Despite these variations, all models proved effective in predicting the robot's performance during a standard 30-second task, demonstrating their adequacy for various industrial applications. This research highlights the balance between computational efficiency and accuracy in model selection. While the detailed model offers the highest precision, it demands more computational resources, which is suitable for high-precision tasks. In discrepancy, simplified, less precise models offer computational efficiency, making them adequate for specific scenarios. Our study provides critical insights into selecting dynamic models in industrial robotics. It guides the optimization of performance and energy efficiency based on the required task precision and available computational resources. This comprehensive comparison of dynamic models underscores their applicability and effectiveness in diverse industrial settings.

Industrial camera model positioned on an effector for automated tool center point calibration

The study presents a novel, full model of an industrial camera suitable for robotic manipulator tool center point (TCP) calibration. The authors propose a new solution which employs a full camera model positioned on the effector of an industrial robotic arm. The proposed full camera model simulates the capture of a calibration pattern for use in automated TCP calibration. The study describes an experimental test robot stand for producing a reference data set, a full camera model, the parameters of a generally known camera obscura model, and a comparison of proposed solution with the camera obscura model. The results are discussed in the context of an innovative approach which features a full camera model to assist the TCP calibration process. The results showed that the full camera model produced greater accuracy, a significant benefit not provided by other state-of-the-art methods. In several cases, the absolute error produced was up to seven times lower than with the state-of-the-art camera obscura model. The error for small rotation (max. of 5^circ ) and small translation (max. of 20 mm) was 3.65 pixels. The results also highlighted the applicability of the proposed solution in real-life industrial processes.

A Calibrator Fuzzy Ensemble for Highly-Accurate Robot Arm Calibration.

The absolute positioning accuracy of an industrial robot arm is vital for advancing manufacturing-related applications like automatic assembly, which can be improved via the data-driven approaches to robot arm calibration. Existing data-driven calibrators have illustrated their efficiency in addressing the issue of robot arm calibration. However, they mostly are single learning models that can be easily affected by the insufficient representation of the solution space, therefore, suffering from the calibration accuracy loss. To address this issue, this study proposes a calibrator fuzzy ensemble (CFE) with twofold ideas: 1) implementing eight data-driven calibrators relying on different sophisticated machine learning algorithms for an industrial robot arm, which guarantees the accuracy of individual base models and 2) innovatively developing a fuzzy ensemble of the obtained eight diversified calibrators to obtain impressively high calibration accuracy for an industrial robot arm. Extensive experiments on an ABB IRB120 industrial robot implemented with MATLAB demonstrate that compared with state-of-the-art calibrators, CFE decreases the maximum error at 8.59%. Hence, it has great potential for real applications.

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

Synchronized Control of Robotic Arm based on Virtual Reality and IMU Sensing

This study introduces a robotic control system that combines virtual reality integration and inertial measurement units (IMU) using mixed reality (MR) devices. The system integrates three main modules: (1) virtual reality (VR), which simulates remote reality by the Hololens2, (2) the wearable IMUs device, which captures the operator's hand movements; and (3) the robotic arm UR5, which is controlled by the user and the VR environment. The virtual and physical systems communicate via a Message Queuing Telemetry Transport (MQQT) communication architecture to establish communication between modules. To introduce a closed-loop control system for the robotic arm, model predictive control (MPC) was achieved with precise path planning to provide a flexible, intuitive, and reliable method to operate the remote-controlled manipulator. To validate the system integration and functions, two demonstrations were conducted: (a) the offline mode, where the VR module of the robotic arm was controlled by the IMUs device to check correctness, and (b) the online mode, where the control command was transferred to UR5 to complete a target mission via artificial potential field (APF) adjustment. The primary outcome of this study was the development of virtual and real industrial robotic arms to test the developed model in VR and shop floor labs.

Multi-Objective Point Motion Planning for Assembly Robotic Arm Based on IPQ-RRT* Connect Algorithm

Six-axis industrial assembly robotic arms are pivotal in the manufacturing sector, playing a crucial role in the production line. The IPQ-RRT* connect motion planning algorithm for the robotic arm is proposed to improve the assembly process by reducing the time of motion planning and improving the assembly efficiency. The new IPQ-RRT* connect algorithm improves the original PQ-RRT* algorithm applied to UAVs in two dimensions by adding a node-greedy bidirectional scaling strategy. An obstacle detection range is set on the node-greedy bidirectional scaling strategy, in which the existence of obstacles is judged, and different sampling strategies are used according to the judgment results to get rid of obstacles faster, while bidirectional sampling can further improve the operation efficiency of the algorithm. In addition, effective collision detection is realized by combining the hierarchical wraparound box method. Finally, the Bezier curve is utilized to smooth the trajectory of the assembly robotic arm, which improves the trajectory quality while ensuring that the assembly robotic arm does not collide with obstacles. This paper takes the actual assembly process of an intelligent assembly platform as an example and proves the feasibility and effectiveness of the algorithm through simulation experiments and real I5 assembly robotic arm experiments.

Exploration-based model learning with self-attention for risk-sensitive robot control

Model-based reinforcement learning for robot control offers the advantages of overcoming concerns on data collection and iterative processes for policy improvement in model-free methods. However, both methods use exploration strategy relying on heuristics that involve inherent randomness, which may cause instability or malfunction of the target system and render the system susceptible to external perturbations. In this paper, we propose an online model update algorithm that can be directly operated in real-world robot systems. The algorithm leverages a self-attention mechanism embedded in neural networks for the kinematics and the dynamics models of the target system. The approximated model involves redundant self-attention paths to the time-independent kinematics and dynamics models, allowing us to detect abnormalities by calculating the trace values of the self-attention matrices. This approach reduces the randomness during the exploration process and enables the detection and rejection of detected perturbations while updating the model. We validate the proposed method in simulation and with real-world robot systems in three application scenarios: path tracking of a soft robotic manipulator, kinesthetic teaching and behavior cloning of an industrial robotic arm, and gait generation of a legged robot. All of these demonstrations are achieved without the aid of simulation or prior knowledge of the models, which supports the proposed method’s universality for various robotics applications.

Cable path optimization by fixing multiple guides on one link for industrial robot arms

Cable path optimization by fixing multiple guides on one link for industrial robot arms

Configurable hybrid integral manufacturing platform: subtractive-additive process with industrial robot arm, proof of concept results

Configurable hybrid integral manufacturing platform: subtractive-additive process with industrial robot arm, proof of concept results

An innovative composite elbow manufacturing method with 6-axis robotic additive manufacturing for fabrication of complex composite structures

Filament winding method is the most commonly used method to produce profiles with different cross sections as composite product manufacturing. In this method, fiber material is wound with resin at different angles on a mold that has a suitable cross section shape. As a winding strategy, angled and helical winding can be done. Motion planning for this process is done with geodesic and nongeodesic theories. Requirement to use mold in the filament winding method increases the cost. Also, there is an obligation to helical windings. In winding of different layers, 90° angle cannot be given between the layers. To overcome all these constraints, UV curing can be achieved using photopolymer resin and continuous fiber glass fiber with the help of robotic additive manufacturing technology. Toolpath strategies for production has a key role in this work. As a tool path strategy, nonplanar slicing can be done and manufactured composite elbow in angular layers without mold. Then, under favour of 6-axis mobility of the industrial robot arm, layers can be obtained at exactly 90° angle. In addition, in this method, unlike other winding methods, internal voids, i.e. a filling rate, can be given within the cylindrical encircled layers. In order to verify whether the elbows produced with this method meet the requirements of the desired applications in the industry in terms of mechanical properties, at different filling rates (50%, 75%, 100%), winding turns (0 and 1/8), and different fiber densities (45%, 55% and 65%) 90° curved composite elbows were produced and their internal pressure strength tests were tested. Afterwards, an optimization study was carried out with the Taguchi method for the production parameters that will maximize the internal pressure strength. According to the results of the optimization study, it is seen that it is appropriate to choose the printing parameters that will obtain the highest internal pressure strength values for production with this method, 100% fill rate, 65% fiber density and 0° winding angle. The products made of this process have the advantage of easy-shaping, reasonable ratio of axial strength and encircled strength, specification easy-unifying, stable product quality.

Analysis of Bifurcation Vibrations of an Industrial Robot Arm System with Joints Compliance

This paper deals with the analysis of nonlinear vibration phenomena in the arm system of an industrial robot. The presented mathematical model of the robot takes into account the flexibility of the joints, which results in low-frequency vibrations in the arm. The study of vibration phenomena has been carried out using analytical, numerical and experimental methods. Special attention has been paid to bifurcation phenomena. The influence of two bifurcation parameters on the behaviour of the robot arm was studied. It was shown that the amplitude and frequency of the harmonic excitation create a nonlinear effect on the vibration amplitudes of the links. In bifurcation regions, the vibration amplitudes can even differ several times depending on the direction of changes in the bifurcation parameter. In general, the results indicate that, contrary to common practice, it is worth paying attention to nonlinear phenomena when analysing robot vibrations.

Cost-effective concrete fabrication for large irregularly shaped architectural structures

The integration of 3D concrete printing (3DCP) technology offers infinite possibilities and a wider palette of geometries for traditional concrete materials. This paper presents a pipeline for 3DCP by optimizing the material used in an irregularly shaped PETG formwork to ensure sufficient stiffness under dynamic loading conditions, while reducing the supporting equipment and costs in the manufacturing industry. The developed framework was validated through a case study that fabricated a concrete wall using a six-axis industrial robotic arm with a custom 3D printing system and parametric design modeling. Comprehensive failure analysis and numerical prediction performance were conducted to validate the proposed pipeline's viability. The formwork reinforcement process encourages smart manufacturing automation and customization, improving production precision. The presented insights offer exciting challenges and inspiration for future manufacturing industries.