CELL CONTROL ENGINEERING

Welding robots play a crucial role in manufacturing industries, where minimizing energy consumption (EC) is increasingly important for enhancing efficiency and reducing operational costs. This study presents a data-driven approach to model and optimize EC in welding robot systems, utilizing a dataset generated from real-world measurements of robot EC during various motions and integrated with trajectory data. A predictive model was developed using an extreme gradient boosting (XGBoost) regression technique focused on joint torque data, which achieved a mean absolute percentage error (MAPE) of 1.86%. Furthermore, trajectory optimization was achieved by adjusting the spatial position of the workpiece, effectively reducing EC. To solve the optimization problem, an improved whale optimization algorithm (IWOA) was employed. Experimental validations with a welding robot demonstrate that the proposed method not only accurately predicted EC with a MAPE of 2.66% but also reduced the robot system’s EC by 6.72%, outperforming the traditional method focused solely on joint motor EC, which achieved a 4.08% reduction. These results confirm the efficacy of the proposed approach, underscoring its potential for broad application in robotic systems to achieve significant energy savings.

Grinding/lapping process is the final process for mold manufacturing, and is the criteria to check the quality of molds. However, this process depends on manual operation due to its work characteristics, and becomes the bottleneck process in the whole mold manufacturing process. To reduce mold manufacturing lead time, and to improve its productivity, multi joint robot system is implemented to automate grinding/lapping process. Total 8-axis redundancy grinding/ lapping robot system is developed as automation system which consists of 6-axis multi joint robot, 1-axis straight line carriage, 1-axis index table for walk rotation. Especially, we developed simulator which enables virtual drive simulation using OLP (Off Ling Programming) by importing 3D mold design data. In this study, we analyzed position error by measuring drive position command value and actual site robot driven automatic teaching using laser tracker, to optimize OLP simulation's operation program for grinding/lapping robot system. Based on this error analysis data, it will be able to compensate position for 3D walk model in virtual environment.

Task level off-line programming system for robotic arc welding — an overview

The use of industrial robots for welding automation is a major and expanding applications area. In a welding task, the robot should smoothly trace along the weldseam with appropriate tool poses. For welding a complex 3-D curve, a large number of points should be taught to make the weld path smooth. This is a very tedious job and is not an easy way to leach the robot. In this paper, the requirements of a CAD system to be used as a robotic off-line arc-welding programming system are discussed. An off-line programming system for such application was developed. The workpieces and the weldseam were represented by analytical geometries. The positions and orientations of the weldgun along the trajectory were analysed by the task requirement of the welding procedure and solved by differential geometry. Calibration process was discussed to ensure programs were properly executed. This system is especially useful for off-line programming for the welding of complex 3-D curves.

This study presents an integrated parametric optimization framework combining the Taguchi method with a multi-objective particle swarm optimization (MOPSO) algorithm to enhance the welding performance of an industrial robotic gas metal arc welding (GMAW) system. The Taguchi method is first employed to identify the most influential parameters and their preliminary optimal levels for welding quality, based on orthogonal array experimentation. These results are then used as the initial population for the MOPSO algorithm, which performs a refined global search to obtain Pareto-optimal solutions balancing multiple, often conflicting, objectives. The optimization focuses on three critical quality indices: depth-to-width (D/W) ratio, heat input, and wire consumption. Experimental results using S400 low-carbon steel demonstrate that the proposed hybrid method outperforms the conventional Taguchi-only approach, achieving a 6.17% increase in D/W ratio, a 17.63% reduction in heat input, and a 15.68% decrease in wire consumption. The findings confirm that the Taguchi-MOPSO hybrid approach effectively improves weld quality, reduces energy and material usage, and enhances overall process efficiency in robotic welding applications.

Recognition and identification of weld environment and seam dimensional position by computer vision is a key technology for developing advanced autonomous welding robot. Aiming at requirements for recognition of weld seam image characteristics, this paper first presents an improved algorithm of subpixel edge detection based on Zernike moments. Comparing with the Ghosal’s original algorithm, the improved algorithm deals with mask effect and first derivative model on edge gradient direction so that it has the strong robust to noise, self-thinning ability and higher locating precision. An algorithm based on ZMs to extract line is also proposed, the comparative results with SHT and RHT show the method has the highest calculation speed and accuracy. The stereovision technology is developed to identify dimensional position of weld seam by computing dimensional coordinates of the weld seam. According to characteristics of weld seam, view field scope model and stereovision model based on baseline are studied and a stereo matching method is presented. In order to evaluate the algorithms and models presented in this paper, a welding robot systems with single camera fixed on the weld torch end-effector has been established for the robot to identify the dimensional position of typical weld seam by one-item and two-position method. The experiment results on S-shape and saddle-shape weld seams show that the vision computing method developed in this paper can be used for acquiring weld seam dimensional position information in welding robot system. Thus the welding path is mapped before the welding operation is executed.

This paper addresses the vision sensing and neuron control techniques for real-time sensing and control of weld pool dynamics during robotic arc welding. Current teaching playback welding robots are not provided with this real-time function for sensing and control of the welding process. In our research, using composite filtering technology, a computer vision sensing system was established and clear weld pool images were captured during robotic-pulsed Gas Tungsten Arc Welding (GTAW). A corresponding image processing algorithm has been developed to pick up characteristic parameters of the weld pool in real-time. Furthermore, an ANN model of the weld pool dynamic process of robotic-pulsed GTAW was developed. Based on neuron self-learning PSD controller design, the real-time control of weld pool dynamics during the pulsed GTAW process has been realized in robotic systems.

Processing configuration off-line optimization for functionally redundant robotic drilling tasks

A novel field box girder welding robot and realization of all-position welding process based on visual servoing

Arc welding robots have rapidly come into widespread use. However, because of the special characteristics of aluminium such as softness, good conductivity and a liability to form an oxide film on the material surfaces, welding itself is inherently difficult and its robotisation has lagged behind other materials. Our company has considered robots and welding equipment together as welding systems and produced aluminium welding robots which are easy to handle and of high reliability. In addition, in our company off‐line teaching is considered to be a more flexible approach to a multiplicity of small quantity production tasks compared with the play back principle which is generally used in present welding robots.

Describes the use of welding robots for making bridge panels. The system uses a total of 14 sets of High Speed Rotating Arc welding robots and newly‐developed arc sensor techniques are used with both joint end and bead end sensors. A teaching‐less direct CADCAM system was developed to control the robots. The welding robot system is now in commercial operation with welding efficiencies that are twice those possible with conventional processes.

Hand-eye calibration is one of the most important and fundamental tasks in performing visual guided robot control tasks. Many techniques have been proposed that use various expensive equipment ranging from a three dimensional positioning device to a laser measurement device. The work presented in this paper aims at developing a low cost robot hand/eye calibration method that is accurate and robust enough to be used in a robotic arc welding system. The proposed calibration method allows the camera and object frames to be referenced directly to the robots base co-ordinate frame. Experimental results show that the developed method can be used to achieve a calibration accuracy of 1 mm (in the directions of interest, X and Y axes) that is acceptable for robotic welding applications.

This research investigates the improvement of trajectory tracking in robotic systems by employing a model-based controller and a fractional-order iterative learning controller (FOILC). Robotic systems are subject to high uncertainty in the mathematical model, which directly impacts the quality of the model-based control strategy. This study focuses on parametric uncertainty in the robot model. The proposed solution capitalizes on the synergies between model-based control and fractional-order iterative learning strategy. FOILC controller's task is to mitigate the influence uncertainty has on trajectory tracking iteratively. Central to this investigation is the tuning of control parameters, a task undertaken by utilizing the Particle Swarm Optimization (PSO) algorithm. By leveraging PSO, the FOILC control parameters are optimized to maximize the system's performance by reducing the trajectory tracking error according to the chosen objective function. The efficiency of the proposed control algorithms is evaluated through extensive simulation on a three degree of freedom robot arm. The findings underscore the performance improvement of the tuned fractional-order iterative learning controller over its integer-order counterpart in trajectory tracking. These results not only validate the efficiency of the FOILC approach but also highlight the significance of fine parameter tuning methodologies in advancing the capabilities of robotic systems based on the dynamics model.

Digital and mechatronic methods, paired with artificial intelligence and machine learning, are transformative technologies in behavioral science and biology. The central element of the most important pollinator species-honey bees-is the colony's queen. Because honey bee self-regulation is complex and studying queens in their natural colony context is difficult, the behavioral strategies of these organisms have not been widely studied. We created an autonomous robotic observation and behavioral analysis system aimed at continuous observation of the queen and her interactions with worker bees and comb cells, generating behavioral datasets of exceptional length and quality. Key behavioral metrics of the queen and her social embedding within the colony were gathered using our robotic system. Data were collected continuously for 24 hours a day over a period of 30 days, demonstrating our system's capability to extract key behavioral metrics at microscopic, mesoscopic, and macroscopic system levels. Additionally, interactions among the queen, worker bees, and brood were observed and quantified. Long-term continuous observations performed by the robot yielded large amounts of high-definition video data that are beyond the observation capabilities of humans or stationary cameras. Our robotic system can enable a deeper understanding of the innermost mechanisms of honey bees' swarm-intelligent self-regulation. Moreover, it offers the possibility to study other social insect colonies, biocoenoses, and ecosystems in an automated manner. Social insects are keystone species in all terrestrial ecosystems; thus, developing a better understanding of their behaviors will be invaluable for the protection and even the restoration of our fragile ecosystems globally.

Service robot has its challenge to adapt on new tasks based on instruction or environment. Such conditions need to reconfigure or reorganize to obtain new system capabilities. However, most common service robot, especially the one that gives services to help human to deliver food or non medical stuffs in an elderly house, suffers on its task flexibility. Unlike a human, robot system needs to be reconfigured to have a new task which may interrupt its services because of this event. To this, we need a technique to handle adaptive tasks that is flexible to change the robot tasks that do not create delay. Dynamic software product line provides a mechanism to reconfigure a system to have different capabilities based on the core assets of the system, such as software that supports and controls the hardware components in a robotic system. In this work, we make use of the dynamic software product line to create a flexible method that enables a service robot to adapt on its new tasks. In order to define the change of tasks, we model the transformation of task using a transition state which has constraints as a trigger. Other than that, the request of change may also have resulted by forcing an interrupt mechanism. To demonstrate the capability of our approach, the proof of concept using Robotic Operating System will be presented.