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Fillet welding is one of the most widespread types of welding in the industry, which is still carried out manually or automated by contact. This paper aims to describe an online programming system for noncontact fillet welding robots with “U”- and “L”-shaped structures, which responds to the needs of the Fourth Industrial Revolution.

In this paper, the authors propose an online robot programming methodology that eliminates unnecessary steps traditionally performed in robotic welding, so that the operator only performs three steps to complete the welding task. First, choose the piece to weld. Then, enter the welding parameters. Finally, it sends the automatically generated program to the robot.

The system finally managed to perform the fillet welding task with the proposed method in a more efficient preparation time than the compared methods. For this, a reduced number of components was used compared to other systems: a structured light 3 D camera, two computers and a concentrator, in addition to the six-axis industrial robotic arm. The operating complexity of the system has been reduced as much as possible.

To the best of the authors’ knowledge, there is no scientific or commercial evidence of an online robot programming system capable of performing a fillet welding process, simplifying the process so that it is completely transparent for the operator and framed in the Industry 4.0 paradigm. Its commercial potential lies mainly in its simple and low-cost implementation in a flexible system capable of adapting to any industrial fillet welding job and to any support that can accommodate it.

In this study, a robotic robust system is achieved, aligned to Industry 4.0, with a friendly, intuitive and simple interface for an operator who does not need to have knowledge of industrial robotics, allowing him to perform a fillet welding saving time and increasing productivity.

Presents an approach to improved performance and flexibility in industrial robotics by means of sensor integration and feedback control in task‐level programming and task execution. Also presents feasibility studies in support of the ideas. Discusses some solutions to the problem using six degrees of freedom force control together with the ABB S4CPlus system as an illustrative example. Consider various problems in the design of an open sensor interface for industrial robotics and discusses possible solutions. Finally, presents experimental results from industrial force controlled grinding.

Flexible automation robotic systems and off‐line programming methods have recently received much attention. Studies the problem of robot auto‐marking and auto‐cutting of shipbuilding panels, using an integrated computer aided design/manufacturing (CAD/CAM) system based on computer technology and off‐line programming of the robot. The following three points are focused on in this paper: marking and cutting information of the panel’s CAD model; measurement of the panel’s deformation and its compensation algorithm; robot auto‐making and auto‐cutting of the panel using the CAM system. Robot auto‐marking and auto‐cutting of shipbuilding panels solves the difficulty associated with panel marking and cutting by hand. Furthermore this system possesses high processing precision and automatically compensates for the deformation of the panel. Our experiments prove the feasibility and efficiency of this system at the end of this paper.

This paper aims to present the critical issues and methodologies to improve robotic machining performance with flexile industrial robots.

A complete solution using active force control is introduced to address various issues during the robotic machining process.

Programming complex couture parts without a CAD model is made easy by using force control functions such as lead‐through and path‐learning. The problem of process control is treated with a novel methodology that consists of stiffness modeling, real‐time deformation compensation for quality and controlled material removal rate for process efficiency.

Experimental results showed that higher productivity as well as better surface quality can be achieved, indicating a promising and practical use of industrial robots for machining applications that is not available at present.

Additive manufacturing (AM) technologies have recently turned into a mainstream production method in many industries. The adoption of new manufacturing scenarios led to the necessity of cross-disciplinary developments by combining several fields such as materials, robotics and computer programming. This paper aims to describe an innovative solution for implementing robotic simulation for AM experiments using a robot cell, which is controlled through a system control application (SCA).

For this purpose, the emulation of the AM tasks was executed by creating a robot working station in RoboDK software, which is responsible for the automatic administration of additive tasks. This is done by interpreting gcode from the Slic3r software environment. Posteriorly, all the SCA and relevant graphical user interface (GUI) were developed in Python to control the AM tasks from the RoboDK software environment. As an extra feature, Slic3r was embedded in the SCA to enable the generation of gcode automatically, without using the original user interface of the software. To sum up, this paper adds a new insight in the field of AM as it demonstrates the possibility of simulating and controlling AM tasks into a robot station.

The purpose of this paper is to contribute to the AM field by introducing and implementing an SCA capable of executing/simulating robotic AM tasks. It also shows how an advanced user can integrate advanced simulation technologies with a real AM system, creating in this way a powerful system for R&D and operational manufacturing tasks. As demonstrated, the creation of the AM environment was only possible by using the RoboDk software that allows the creation of a robot working station and its main operations.

Although the AM simulation was satisfactory, it was necessary to develop an SCA capable of controlling the whole simulation through simple commands instructed by users. As described in this work, the development of SCA was entirely implemented in Python by using official libraries. The solution was presented in the form of an application capable of controlling the AM operation through a server/client socket connection. In summary, a system architecture that is capable of controlling an AM simulation was presented. Moreover, implementation of commands in a simple GUI was shown as a step forward in implementation of modern AM process controls.

Automated robotic assembly on a moving workpiece, referred to as assembly in motion, demands that an assembly robot is synchronised in all degrees of freedom to the moving workpiece, on which assembly parts are installed. Currently, this requirement cannot be met due to the lack of robust estimation of 3D positions and the trajectory of the moving workpiece. The purpose of this paper is to develop a camera system that measures the 3D trajectory of the moving workpiece for robotic assembly in motion.

For the trajectory estimation, an assembly robot-guided, monocular camera system is developed. The motion trajectory of a workpiece is estimated, as the trajectory is considered as a linear combination of trajectory bases, such as discrete cosine transform bases.

The developed camera system for trajectory estimation is tested within the robotic assembly of a cylinder block in motion. The experimental results show that the proposed method is able to reconstruct arbitrary trajectories of an assembly point on a workpiece moving in 3D space.

With the developed technology, a point trajectory can be recovered offline only after all measurement images are acquired. For practical assembly tasks in real production, this method should be extended to determine the trajectory online during the motion of a workpiece.

For practical, robotic assembly in motion, such as assembling tires, wheels and windscreens on conveyed vehicle bodies, the developed technology can be used for positioning a moving workpiece, which is in the distant field of an assembly robot.

Besides laser trackers, indoor global positioning systems and stereo cameras, this paper provides a solution of trajectory estimation by using a monocular camera system.

Sensorless passive lead-through programming (LTP) is a promising physical human-robot interaction technology that enables manual trajectory demonstrations based on gravity and friction compensation. The major difficulty lies in static friction compensation during LTP start-up. Instead of static friction compensation, conventional methods only compensate for Coulomb friction after the joint velocity exceeds a threshold. Therefore, conventional start-up external torques must overcome static friction. When the static friction is considerable, it is difficult for conventional LTP to start up and make small movements. This paper aims to decrease the start-up external torque and improve the small movement performance.

This paper reveals a novel usage of a high-gain position-loop in industrial robot applications aimed at sensitively detecting external torque during start-up. Then, the static friction is partly compensated by Coulomb friction to facilitate start-up. In addition, a detailed transition method between the proposed start-up and conventional passive LTP is proposed based on a finite state machine.

Experiments are implemented on the ROKAE XB4 robot to verify the effectiveness of the proposed external torque detection. Compared with the conventional LTP method, the proposed LTP method significantly decreases the start-up external torque and facilitates small movements.

This paper proposes and verifies a novel start-up method of sensorless LTP based on a start-up external torque detection and a transition method between start-up and conventional LTP. This research improves the LTP start-up performance, especially for industrial robots with large static friction.

Discusses the forthcoming installation of an ABB Flexible Automation beam preparation system at an offshore platform construction company in Scotland. Using a six‐axis robot suspended from a boom, the system will be able to cut beams to any length with any desired end profile using an Arithmetic Robot Application Control [ARAC] system, which allows virtually any beam profile to be programmed into the system, in seconds by means of ARAC macros. Prior to cutting, the robot will exchange its gas torch for a 3‐D probe to measure the actual beam dimensions and compare these with the stored data design. Describes how the ARAC software package works and gives examples of other applications such as the cutting of round tubes for structural assemblies, multi‐pass welding and tube‐to‐sheet welding.

Industrial robots are extensively deployed to perform repetitive and simple tasks at high speed to reduce production time and improve productivity. In most cases, a compliant gripper is used for assembly tasks such as peg-in-hole assembly. A compliant mechanism in the gripper introduces flexibility that may cause oscillation in the grasped object. Such a flexible gripper–object system can be considered as an under-actuated object held by the gripper and the oscillations can be attributed to transient disturbance of the robot itself. The commercially available robots do not have a control mechanism to reduce such induced vibration. Thus, this paper aims to propose a contactless vision-based approach for vibration suppression which uses a predictive vibrational amplitude error-based second-stage controller.

The proposed predictive vibrational amplitude error-based second-stage controller is a real-time vibration control strategy that uses predicted error to estimate the second-stage controller output. Based on controller output, input trajectories were estimated for the internal controller of the robot. The control strategy efficiently handles the system delay to execute the control input trajectories when the oscillating object is at an extreme position.

The present controller works along with the internal controller of the robot without any interruption to suppress the residual vibration of the object. To demonstrate the robustness of the proposed controller, experimental implementation on Asea Brown Boveri make industrial robot (IRB) 1410 robot with a low frame rate camera has been carried out. In this experiment, two objects have been considered that have a low (<2.38 Hz) and high (>2.38 Hz) natural frequency. The proposed controller can suppress 95% of vibration amplitude in less than 3 s and reduce the stability time by 90% for a peg-in-hole assembly task.

The present vibration control strategy uses a camera with a low frame rate (25 fps) and the delays are handled intelligently to favour suppression of high-frequency vibration. The mathematical model and the second-stage controller implemented suppress vibration without modifying the robot dynamical model and the internal controller.