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Position of the end-of-arm reference point: (a) start (measuring) position; (b) endpoint of the trajectory.
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Positioning accuracy in robotics is a key issue for the manufacturing process. One of the possible ways to achieve high accuracy is the implementation of machine learning (ML), which allows robots to learn from their own practical experience and find the best way to perform the prescribed operation. Usually, accuracy improvement methods cover the g...
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Real-time and efficient path planning is critical for all robotic systems. In particular, it is of greater importance for industrial robots since the overall planning and execution time directly impact the cycle time and automation economics in production lines. While the problem may not be complex in static environments, classical approaches are i...
Citations
... In [11], an experimental study of the vibrations of a roller shutter gripper on a robotic palletizing station was presented using a FANUC Robotguide environment, demonstrating effective vibration reduction. In [12], Bucinskas et al. provide a methodology for the online deep Q-learning-based approach intended to increase positioning accuracy at key points by analyzing experimentally predetermined robot properties and their impact on overall accuracy. The KUKA-YouBot robot has been used, and the proposed ML-based compensation method resulted in a positioning error decrease at the trajectory of 30% of the tolerance declared. ...
Robot acceptance is rapidly increasing in many different industrial applications. The advancement of production systems and machines requires addressing the productivity complexity and flexibility of current manufacturing processes in quasi-real time. Nowadays, robot placement is still achieved via industrial practices based on the expertise of the workers and technicians, with the adoption of offline expensive software that demands time-consuming simulations, detailed time-and-motion mapping activities, and high competencies. Current challenges have been addressed mainly via path planning or robot-to-workpiece location optimization. Numerous solutions, from analytical to physical-based and data-driven formulation, have been discussed in the literature to solve these challenges. In this context, the machine learning approach has proven its superior performance. Nevertheless, the industrial environment is complex to model, generating extra training effort and making the learning procedure, in some cases, inefficient. The industrial problems concern workstation productivity; path-constrained minimal-time motions, considering the actuator’s torque limits; followed by robot vibration and the reduction in its accuracy and lifetime. This paper presents a procedure to find the robot base location for a prescribed task within the robot’s workspace, complying with multiple criteria. The proposed hybrid procedure includes analytical, physical-based, and data-driven modeling to solve the optimization problem. The contribution of the algorithm, for a given user-defined task, is the search for the best robot base location that enables the target points, maximizing the manipulability, avoiding singularities, and minimizing energy consumption. Firstly, the established method was verified using an anthropomorphic robot that considers different levels of a priori kinematics and system dynamics knowledge. The feasibility of the proposed method was evaluated through various simulations for small- and medium-sized robots. Then, a commercial offline program was compared, considering three scenarios and fourteen robots demonstrating an energy reduction in the 7.6–13.2% range. Moreover, the unknown joint dependency in real robot applications was investigated. From 11 robot positions for each active joint, a direct kinematic was appraised with an automatic DH scheme that generates the 3D workspace with an RMSE lower than 65.0 µm. Then, the inverse kinematic was computed using an ANN technique tuned with a genetic algorithm showing an RMSE in an S-shape task close to 702.0 µm. Finally, three experimental campaigns were performed with a set of tasks, repetitions, end-effector velocity, and payloads. The energy consumption reduction was observed in the 12.7–22.9% range. Consequently, the proposed procedure supports the reduction in workstation setup time and energy saving during industrial operations.
... C OMPARED to conventional specialized equipment, industrial robots offer a larger workspace, a greater degree of freedom, excellent scalability, and a low cost [1], [2]. They are widely used for tasks such as handling, assembly, and welding. ...
Industrial robots are extensively utilized in handling, assembly, and welding tasks owing to their expansive workspace, scalability, flexibility, and cost-effectiveness. However, their inadequate absolute positioning accuracy significantly impedes their application in precise operational scenarios. To enhance robot positioning accuracy, the hysteresis error induced by gear meshing backlash is considered. Firstly, the impact of joint hysteresis on robot positioning errors is analyzed, the notion of modified joint space is introduced, and the similarity theory of error in modified joint space is analyzed. Secondly, for the problem of parameter overfitting of the universal Kriging model, a method of dynamically determining the basis function set by using the genetic algorithm is proposed. Finally, the target trajectory is corrected by a feed-forward iterative compensation algorithm. An experiment on a tandem industrial robot SMART5 NJ 220-2.7 is conducted to demonstrate the effectiveness of the compensation. The experimental results show that the error caused by joint hysteresis is significant, with joint 1 notably affecting y axis positioning accuracy, while joints 2 and 3 predominantly influence x axis positioning accuracy. Furthermore, cross-validation tests verified the good anti-overfitting effect of optimized Kriging for models with multiple input parameters and the good fitting accuracy of the modified space model for hysteresis errors. Moreover, after employing MJS&GPS+GA error modeling and feed-forward iteration compensation, the average absolute positioning error of the trajectory decreased by 81% to 0.09252 mm, and the maximum absolute positioning error decreased by 59% to 0.27713 mm.
... The implementation of ML can increase positioning accuracy in robotics [66]. A methodology for an online deep Q-learning-based approach is presented, which experimentally analyzes predetermined robot properties and their impact on overall accuracy. ...
Recently, the need to produce from soft materials or components in extra-large sizes has appeared, requiring special solutions that are affordable using industrial robots. Industrial robots are suitable for such tasks due to their flexibility, accuracy, and consistency in machining operations. However, robot implementation faces some limitations, such as a huge variety of materials and tools, low adaptability to environmental changes, flexibility issues, a complicated tool path preparation process, and challenges in quality control. Industrial robotics applications include cutting, milling, drilling, and grinding procedures on various materials, including metal, plastics, and wood. Advanced robotics technologies involve the latest advances in robotics, including integrating sophisticated control systems, sensors, data fusion techniques, and machine learning algorithms. These innovations enable robots to adapt better and interact with their environment, ultimately increasing their accuracy. The main focus of this study is to cover the most common industrial robotic machining processes and to identify how specific advanced technologies can improve their performance. In most of the studied literature, the primary research objective across all operations is to enhance the stiffness of the robotic arm’s structure. Some publications propose approaches for planning the robot’s posture or tool orientation. In contrast, others focus on optimizing machining parameters through the utilization of advanced control and computation, including machine learning methods with the integration of collected sensor data.
... This technique effectively enhances the positioning accuracy of aerial robots. Although these approaches focus on error compensation from the perspective of the robot's kinematic accuracy, several other scholars [23,24] have conducted extensive research in this area. While these algorithms have contributed to improving the positioning accuracy of robots to some extent, their efficacy is limited and requires extensive experimental validation. ...
In the pursuit of automating the entire underground drilling process in coal mines, the automatic rod feeding technology of drilling robots plays a crucial role. However, the current lack of positional accuracy in automatic rod feeding leads to frequent accidents. To address this issue, this paper presents an algorithm for compensating positioning errors in automatic rod feeding. The algorithm is based on a theoretical mathematical model and manual teaching methods. To enhance the positioning accuracy, we first calibrate the pull rope sensor to correct its measurement precision. Subsequently, we establish a theoretical mathematical model for rod feeding positions by employing spatial coordinate system transformations. We determine the target rod feeding position using a manual teaching-based approach. Furthermore, we analyze the relationship between the theoretical rod delivery position and the target rod delivery position and propose an anisotropic spatial difference compensation technique that considers both distance and direction. Finally, we validate the feasibility of our proposed algorithm through automatic rod feeding tests conducted on a coal mine underground drilling robot. The results demonstrate that our algorithm significantly improves the accuracy of rod feeding positions for coal mine underground drilling robots.
... Incorporating the sensors with image analysing techniques in real time monitoring is supported by Machine learning model. Bucinskas et al. [26] mentioned that positioning accuracy of industrial robot is an important problems in manufacturing. To achieve the high accuracy in the manufacturing field need to apply the Machine learning approach. ...
Industrial robots are used for various industrial applications which requires the high level of accuracy of the movement and repeatability of the operations in the shop floor. To maintain the accuracy, it is very important to measure the positional error of the robot by using a dial gauge indicator. The noise factors like Environmental conditions, machine wear, friction between parts and calibration issues may influence the repeatability and accuracy of the robot. This research is designed to evaluate and analyse the accuracy and repeatability of IRB1410 Robot using L9 Taguchi design of experiments. The major influencing parameters of robot being manoeuvring speed, the distance of movement and payload are optimized which affects the accuracy and repeatability of the robot. A Robotic simulation is carried out to check the maneuvering ability of the robot by rapid programming on a controller to run it on a fixed path, a linear motion, on a virtual platform and check the variability of the setup of the experiments. The stability of the motion of the robot is experimented using tools like dial indicator in industrial robot itself. A carefully repeated motion is fed into the robot using Flex pendant controller to fulfil the purpose of testing the repeatability and accuracy of the industrial robot. The ANOVA analysis is carried out to obtain the most influencing robot parameters on the accuracy of the Robot. A statistical analysis of robot position error is analysed by using a polynomial regression model of machine learning. The speed of the robot is a major influencing parameter of robot positional error with less than 5%.
... As stated earlier, anomaly behavior happens when there is unlike frequency happening at a time. Thus, when the mean, standard deviation, or PPV value produced differs from the normal state, it can be categorized as an anomaly, as stated by a study in [17,18]. ...
