Search Results (17)

This paper proposes an adaptive residual extended Kalman filter method optimized by a multi-strategy improved parrot optimization algorithm (MIPO-ARKEKF) to improve the kinematic parameter calibration accuracy and efficiency of robotic polishing systems. To address the limitations of the standard extended Kalman filter (EKF), such as truncation-error accumulation during repeated linearization and sensitivity to manually selected noise parameters, an integrated improvement framework is developed. Specifically, a gradient stabilizer based on state-estimation increments is introduced to alleviate estimation degradation caused by accumulated truncation errors, while the proposed MIPO algorithm is employed to adaptively optimize the process and measurement noise covariance matrices, thereby improving the robustness of parameter identification under practical measurement uncertainty. The calibration process is established on the basis of high-precision external measurement data obtained from the robotic polishing system. In benchmark-function tests, MIPO demonstrates superior convergence performance. In physical experiments based on a KUKA KR210 R2700 robot, the proposed MIPO-ARKEKF method reduces the root mean square positioning error from 0.8927 mm to 0.4858 mm, corresponding to a 45.58% improvement in accuracy. Compared with representative hybrid calibration methods, the proposed method achieves comparable compensation accuracy while reducing computation time by 34.88% to 65.08%. Practical polishing experiments on ultra-low-expansion glass lenses further verify that the proposed method effectively improves end-effector trajectory tracking accuracy and polishing quality, providing an efficient solution for high-precision robotic polishing. Full article

This paper presents a low-cost retrofitting pipeline for a legacy industrial robot that uses a single RGB webcam and monocular 2D keypoint tracking to estimate human-arm posture angles ${\mathit{\theta }}^{\left(\mathrm{h}\right)}$ and map them to robot-axis joint targets ${\mathbf{q}}_{\mathrm{cmd}}^{\left(\mathrm{r}\right)}$ for A1–A3 on a KUKA KR5-2 ARC HW, while keeping the wrist orientation (A4–A6) fixed. Rather than targeting full six-DoF manipulation, the main contribution is an experimental characterization of how far monocular 2D posture-to-axis mapping can be used reliably for coarse placement and safeguarded low-speed demonstrations on a legacy robot platform. Vision-side accuracy was evaluated per axis against goniometer-based reference angles ${\mathit{\theta }}_{\mathrm{ref}}^{\left(\mathrm{h}\right)}$, showing low errors for A2–A3 within the tested range and larger errors for A1 due to monocular yaw/depth ambiguity and occlusions. The study also analyzes failure modes during simultaneous multi-joint motion, where performance degrades notably, especially for A2 and A3, and reports practical mitigation directions such as improved viewpoints, multi-view/depth sensing, and stricter dropout handling. Runtime behavior is additionally characterized through a loop timing budget, with an end-to-end latency of 185.44 ms and an effective loop frequency of 5.39 Hz, which is consistent with low-speed online operation within the demonstrated scope. The system was implemented in a fenced industrial cell with restricted access and emergency stop; no collaborative operation is claimed. Full article

Industrial robots offer flexibility and cost advantages in machining applications but suffer from limited structural stiffness and dynamic instability, leading to significant positional errors. This study presents a simulation-driven framework for automated toolpath compensation in robotic machining, integrating computer-aided design, manufacturing, and engineering environments. Finite Element Analysis is employed to predict stress, deformation, and reaction forces during machining. These predictions guide dynamic adjustments to key process parameters, such as feed rate and spindle speed, to optimize performance and accuracy. An automated optimization procedure streamlines this process, enhancing toolpath efficiency and safety. The framework is validated through a case study involving the machining of an aluminum support bracket using a KUKA KR3 robot. Simulation results demonstrate significant improvements in path accuracy, shorter machining time and enhanced surface quality. The enhanced toolpath achieves a 10–15% reduction in non-cutting movements, a 5–10% improvement in surface finish and a 15–25% decrease in machining time compared to the initial configuration. This approach eliminates the need for hardware modifications or real-time sensors, providing a flexible and modular solution for achieving high precision outcomes in robotic machining. The work presents an automated methodology for compensating multi-source errors, bridging the gap between virtual analysis and physical execution. Full article

Robots are used more and more in manufacturing, especially in tasks like robotic machining, where understanding their vibration behavior is very important. However, robot vibrations vary with posture, and evaluating all representative postures requires significant time and cost. This study proposes a deep learning (DL) based transfer learning (TL) approach to predict robot vibration behavior using fewer experiments. A large dataset was collected from a KUKA KR300 robot (Robot A) by testing nearly 250 postures. This dataset was then used to train a model to predict modal parameters such as natural frequencies (ω_n), damping ratios (ξ), and modal stiffness (k) within the workspace. TL was then used to apply the knowledge from Robot A to two other robots: a Comau NJ 650-2.7 (Robot B, high-payload) and an ABB IRB 4400 (Robot C, low-payload). Only a small number of postures were tested for Robots B and C. They were chosen carefully to cover different workspace areas and avoid collisions. Hammer tests were performed, and a four-step process was used to identify the real vibration modes. Stabilization diagrams were applied to confirm valid modes and remove noise. The results show that TL can accurately predict modal parameters for both Robot B and Robot C, even with limited data. These predictions were also used to estimate frequency response functions (FRFs), which matched well with experimental results. The main novelties of this work are: achieving accurate prediction of posture-dependent dynamics using minimal experimental data, demonstrating generalization across robots with different payload capacities, and revealing that data coverage across the workspace is more critical than dataset size. Full article

The huge growth in the utilisation of six-axis robots in various technological applications in production calls for a detailed focus on the process of preparing Numerical Control (NC) programmes for effective robot control. Considerable attention is currently being paid to optimisation by increasing stiffness, but there is also a need to focus on reducing energy consumption in robot control. Focusing on reducing energy consumption is highly justified given the widespread adoption of robotic systems across diverse manufacturing technologies and the significant potential for application. This is particularly relevant today, when minimising production costs is a critical industrial objective. A redundant degree of freedom—which is the possibility to rotate around the end-effector axis and thus influence the adjustment of the rotation of the individual robot joints—can be used for this purpose. Therefore, this paper exploits this redundant degree of freedom to set up a proper robot configuration that reduces energy consumption. The user-friendly solution, including the algorithm design and processing through a function, could be effectively implemented within an industry-standard post-processor solution for generating NC programmes for robots. This solution is unique as it is used for the optimisation of the working section of the toolpaths, where continuous control of the end-effector movement during manufacturing operations occurs. The solution was verified on a KUKA KR60 HA robot; however, it is applicable to any industrial six-axis robot. Substantial energy savings were obtained in multi-axis toolpath operations, with a 7.5% reduction in total energy consumption when using the optimised NC programme. Full article

Global climate change mitigation has prompted the construction sector to pursue decarbonization strategies, with timber structures offering significant carbon reduction potential. Wood serves as a sustainable material that sequesters carbon during growth while reducing emissions across the entire construction supply chain. Robotic construction of timber structures is increasingly promoted as a low-carbon, intelligent alternative for small- and medium-scale projects, yet the energy consumption and environmental impacts of robotic automated assembly using self-tapping screws remain understudied. This study presents a construction-phase life-cycle assessment (LCA) of an innovative vertically mobile robotic construction system for automated timber structure. The system integrates a KUKA KR 6 R900 (KUKA Robotics Corporation, Augsburg, Germany) six-axis robot with an electrically actuated lifting platform and specialized end-effector, enabling fully autonomous assembly of a Layered Interlaced Timber Arch-Shell (LITAS) structure using Hinoki cypress timber and self-tapping screws. This research provides the first comprehensive LCA dataset for robotic screw-fastened timber construction and establishes a replicable framework for sustainable automated building practices, with methodology scalability enabling application to diverse timber construction scenarios and advancing intelligent and decarbonized transformation in the construction industry. Full article

In addressing the limited absolute positioning accuracy of industrial robots, which stems from the discrepancy between the nominal kinematic model and the physical entity, this paper proposes a novel paradigm for online compensation based on data-driven error prediction. The present study utilized a KUKA KR4 R600 robot as the experimental platform to construct a high-fidelity digital twin system capable of real-time synchronization. Within this framework, a new machine learning model, termed the Global Configuration-Error Forest (GCE-Forest), was developed and validated. The fundamental principle of GCE-Forest, based on the Random Forest algorithm, is its offline learning of the complex, highly non-linear mapping from the robot’s six-dimensional joint space configuration to its three-dimensional end-effector Cartesian error space. This facilitates online, feedforward, and predictive compensation for the nominal trajectory during robot operation. Through rigorous comparative experiments, the superiority of the proposed GCE-Forest was established. The final outcomes of dynamic trajectory tracking validation demonstrate that the system, by accurately predicting a mean nominal error of 0.1977 mm, successfully reduced the average spatial positioning error of the end-effector to 0.0845 mm, achieving an accuracy improvement of 57.25%. This research provides comprehensive validation of the method’s robust performance, offering a low-cost, non-invasive, and highly effective solution for significantly enhancing robotic accuracy. Full article

The aim of this paper is to identify the most suitable material model for the numerical simulation of the incremental forming of polymeric materials using the finite element method. The analysis program used was Ls-Dyna, and two material models, namely material 24 (Piecewise Linear Plasticity) and material 89 (Plasticity Polymer), were chosen for comparison from the library of the program. A comparison was made between two polymeric materials, polyamide PA 6.6 and polyethylene HDPE 1000, with the following dimensions of the forming tools: punch diameter, D_p = 6 mm; die length, L_d = 190 mm; die radius, R_d = 5 mm; die corner radius, R_corner = 10 mm; and blankholder length, L_bl = 190 mm. The simulation using the finite element method was performed with the Ls-Dyna software, and the experimental research was carried out using the Kuka KR210-2 robot. The strains were measured with the Aramis 2M optical system. Experimental investigations were carried out simultaneously, and the results obtained were compared in terms of main strains, thickness reduction, and forces on three directions. Close results were obtained between theoretical and experimental research for both material models. Full article

This study addresses the challenge of accurately modeling the dynamic behavior of industrial robots for precision manufacturing applications. Using a comprehensive experimental approach with modal impulse hammer testing and triaxial acceleration measurements, 360 frequency response functions were recorded along orthogonal measurement paths for a KUKA KR10 robot. Two dynamic models with different parameter dimensions (12-parameter and 24-parameter) were developed in Matlab/Simscape, and their parameters were identified using genetic algorithm optimization. The KUKA KR10 features Harmonic Drives at each joint, whose high transmission ratio and zero backlash characteristics significantly influence rotational dynamics and allow for meaningful static structural measurements. Objective functions based on the Frequency Response Assurance Criterion (FRAC) and Root Mean Square Error (RMSE) metrics were employed, utilizing a frequency-dependent weighting function. The performance of the models was evaluated across different robot configurations and frequency ranges. The 24-parameter model demonstrated significantly superior performance, achieving 70% overall average Global FRAC in the limited frequency range (≤200 Hz) compared to 41% for the 12-parameter model when optimized using a representative subset of 9 measurement points. Both models showed substantially better performance in the limited frequency range than in the full spectrum. This research provides a validated methodology for dynamic characterization of industrial robots and demonstrates that higher-dimensional models, incorporating transverse joint compliance, can accurately represent robot dynamics up to approximately 200 Hz. Future work will investigate nonlinear effects such as torsional stiffness hysteresis, particularly relevant for Harmonic Drive systems. Full article

The use of Hierarchical Systems (HS) technology in the conceptual design of the RWC (Robotic Work Cell) is proposed in the work. In comparison with other widespread approaches, the conceptual model of the RWC constructed in the HS formal basis contains connected models of RWC subsystems, their processes, the RWC structure, its dynamic presentation as the unit in its environment, and the RWC coordinator. The design and control system of RWC is presented in the form of an HS coordinator. For the detailed design of the selected RWC, the Visual Components system was applied in the work. First, the conceptual model of the RWC is presented in the paper. The application of the Visual Components program system for the detailed design of the RWC is described after that. Third, the laboratory experiment with the KUKA KR16-2 F robot is briefly considered. The originality of the proposed work lies in the application of the novel HS technology in the creation of the conceptual model and design of the selected RWC. The model developed at the conceptual design phase is coordinated with the model created at the detailed design phase within the framework of the Visual Components system. The effectiveness of the proposed HS approach in comparison with other known design, AI, and mathematical methods lies in the possibility of solving RWC synthesis and analysis design problems within the framework of one common formal HS model using an HS coordinator that connects the structure of the system being designed with its function, predicting, in this way, the system’s dynamics in its environment, and performing RWC control. The reliability of the model proposed was verified while performing the design and control tasks of the presented RWC and various other mechatronic objects. The results and conclusive remarks are finally presented in the paper. Full article

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. Full article

Recently, industrial robots are mostly used in many areas because of their high dexterity and low price. Nevertheless, the low performance of robot stiffness is the primary limiting factor in machining applications. In this paper, a new method for identifying the joint stiffness of serial robots. The method considers the coupling of the end-effector’s rotational and translational displacements. Then, a new criterion is presented for optimizing the robot configuration. Next, the influence of each joint on the criterion is analyzed, and the corresponding joint is selected to simplify the experiment. After that, the method’s robustness, the sensitivity of the number of tests, and the experimental errors are analyzed. Finally, the KUKA KR60-3 robot is used as a descriptive example. Full article

The machining system based on an industrial robot is a new type of equipment to meet the requirements of high quality, high efficiency and high flexibility for large and complex components of aircraft and spacecraft. The error compensation technology is widely used in robotic machining to improve the positioning accuracy of an industrial robot with the intention of meeting the precision requirements of aerospace manufacturing. However, the robot’s positioning accuracy decreases significantly when the orientation of the tool changes dramatically. This stems from the fact that the existing robot compensation methods ignore the uncertainties of Tool Center Point (TCP) calibration. This paper presents a novel regionalized compensation method for improving the positioning accuracy of the robot with calibration uncertainties and large orientation variation of the TCP. The method is experimentally validated through the drilling of curved surface parts of plexiglass using a KUKA KR2830MT robot. Compared with a published error compensation method, the proposed approach improves the positioning accuracy of the robot under the large orientation variation to 0.235 mm. This research can broaden the field of robot calibration technology and further improve the adaptability of robotic machining. Full article

The development of serial or parallel manipulator robots is constantly increasing due to the need for faster productivity and higher accuracy. Therefore, researchers have turned to combining both mechanisms, sharing the advantage from serial to parallel or vice versa. This paper proposes a new configuration design for a serial-parallel hybrid manipulator (SPHM) using the industrial robotic KUKA Kr6 R900 and 3-DOF parallel spherical mechanism. The Kr6 R900 has six degrees of freedom (6-DOF) divided into three joints for translation (x, y, z) and another three joints for orientation (A, B, C) of the end-effector and the 3-DOF parallel spherical mechanism with three paired links. On the contrary, each limb of the parallel spherical mechanism consists of revolute–revolute–spherical joints (3-RRS). This mechanism allows translation movement along the Z-axis and orientation movements about the X- and Y- axes. The new hybrid will enrich the serial manipulator in movement flexibility and expand the workspace for serial and parallel manipulator robots. In addition, a complete conceptual design is presented in detail for the new robot configuration with a schematic and experimental setup. Then, a comprehensive mathematical model was derived and solved. The forward, inverse kinematics, and workspace analyses were derived using the graphical solution. Additionally, the new hybrid manipulator was tested for path planning. Moreover, an experimental setup was prepared to test the selected path. Finally, the new robot configuration can enlarge the workspace of both manipulators and the selected path matched to the experimental test. Full article

The paper presents issues associated with the experimental study of the vibration of a spot welding gun mounted on a robotic arm. The main aim of the study was to assess the vibration of the robot flange and the vibration of the mounted tool. Because of the tools’ large size and weight (up to 150 kg), manipulating it in a limited space is a challenge for programmers when defining trajectories. The article presents the results of inertial measurements of the KUKA KR120 R2500 industrial robot equipped with a pneumatic welding tool, paying particular attention to the vibrations occurring at the process points. Inertial tests on the robotic station were made using triaxial accelerometers and a high-speed camera. The methodology developed by the authors confirmed the existence of structural vibrations and allowed for defining the relationship between the robot’s motion parameters (notably velocity and acceleration) and the size of the vibrations present. The paper presents selected test results for various parameters of robot motion (speeds from 2000 mm/s to 500 mm/s and acceleration ramps ranging from 100% to 25%). In the course of the study, a disturbance was noticed in the form of a reduction in the value of maximum acceleration. This could be attributed to the appearance of the structure’s natural vibrations. Their character is not constant, and they are damped. Full article