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The purpose of this paper is to review the progress made in arc welding automation using trajectory planning, seam tracking and control methodologies.

This paper discusses key issues in trajectory planning towards achieving full automation of arc welding robots. The identified issues in trajectory planning are real-time control, optimization methods, seam tracking and control methodologies. Recent research is considered and brief conclusions are drawn.

The major difficulty towards realizing a fully intelligent robotic arc welding system remains an optimal blend and good understanding of trajectory planning, seam tracking and advanced control methodologies. An intelligent trajectory tracking ability is strongly required in robotic arc welding, due to the positional errors caused by several disturbances that prevent the development of quality welds. An exciting prospect will be the creation of an effective hybrid optimization technique which is expected to lead to new scientific knowledge by combining robotic systems with artificial intelligence.

This paper illustrates the vital role played by optimization methods for trajectory design in arc robotic welding automation, especially the non-gradient approaches (those based on certain characteristics and behaviour of biological, molecular, swarm of insects and neurobiological systems). Effective trajectory planning techniques leading to real-time control and sensing systems leading to seam tracking have also been studied.

The paper aims to present several methods that were developed, evaluated and finally used as part of a 3D electronic tailor especially adapted to the clothing industry.

An experimental top down approach taking care of building a system adapted to the constraints of the textile industry was used. The research was to the rapidity, the robustness and the comfort of the future system during the development cycle.

A robust and efficient method for digitizing a human body in 3D that is usable for the measurement process with duration and accuracy adapted to the domain of textile industry.

The research is bound to many constraints. Some are expressed by the customers of the electronic tailor, some depend on the manufacturing process of the clothes and of course, some depend on economic requirements. Of course, the system is not fixed because it must be adapted and improved to be able to follow the evolution of the manufacturing process.

This research permitted the creation of a marketed product improved for a few years by successfully measuring thousands of people.

The paper demonstrates the usefulness of choosing a digitizing process. It shows the importance of keeping in mind the whole digitizing process for making the mesh generation and the measurements taken. The resulting mannequin proves that the process works well.

Currently, the majority of designed robots are not well‐matched to their applications because designers do not employ a clear and organized design process. Additionally, the high cost of robotic systems makes it difficult to financially justify the use of this technology. The purpose of this paper is to present a new design process that gathers conceptual, kinematic and dynamic design, finite elements method (FEM), functional design and virtual reality control. Furthermore, kinematic and dynamic design can be obtained by traditional theory or standard computer tools (SCT) to accelerate the design. Through SCT fitted mathematical models and non‐mathematical virtual models may be acquired.

This paper investigates the design process of a robot. First, the entire methodology is presented (including two new techniques for solving the kinematic and dynamic questions via SCT). Second, a case study using Autodesk^® Inventor™ has been analysed to assess the feasibility of the method and techniques.

The more stages of the design process are considered, the more successful solutions become. Designers can obtain a mathematical solution for an analytically unsolvable robot fitting a mathematical model by SCT. To obtain a rapid design, designers must consider using SCT and following just in need (JIN) philosophy to find a non‐mathematical virtual model.

This paper presents an innovative guide for robotic engineers and researchers which covers the whole design process and new techniques for obtaining mathematical and non‐mathematical solutions.

Predicting the final status of an ongoing process or a subsequent activity in a process is an important aspect of process management. Semi-structured business processes cannot be predicted by precise and mathematical methods. Therefore, artificial intelligence is one of the successful methods. This study aims to propose a method that is a combination of deep learning methods, in particular, the recurrent neural network and Markov chain.

The proposed method applies the BestFirst algorithm for the search section and the Cfssubseteval algorithm for the feature comparison section. This study focuses on the prediction systems of social insurance and tries to present a method that is less costly in providing real-world results based on the past history of an event.

The proposed method is simulated with real data obtained from Iranian Social Security Organization, and the results demonstrate that using the proposed method increases the memory utilization slightly more than the Markov method; however, the CPU usage time has dramatically decreased in comparison with the Markov method and the recurrent neural network and has, therefore, significantly increased the accuracy and efficiency.

This research tries to provide an approach capable of producing the findings closer to the real world with fewer time and processing overheads, given the previous records of an event and the prediction systems of social insurance.

The purpose of this paper is to present systematic optimal design procedures for the Gough-Stewart platforms used as engineering motion simulators.

Three systematic optimal design procedures are proposed to solve the engineering design problems for the Gough-Stewart platform used as motion simulators. In these systematic optimal design procedures, two contradicting design optimality criteria with good representations of performances of the Gough-Stewart platforms are chosen as the objective functions. In addition, the two objective function optimization problems are solved by using the multi-objective evolutionary algorithms.

In the systematic optimal design procedures, multiple compromised design solutions are found by using Elitist Non-Dominated Sorting Genetic Algorithm version II in the primary design stage, and many candidates can be used in the secondary design stage for higher decisions. Two higher decision methods have been presented to choose the final solutions.

This paper proposes three systematic optimal design procedures to solve the practical design problems of the Gough-Stewart platforms used as motion simulators, which are very important for the engineering designers.

Due to dynamic model is the basis of realizing various robot control functions, and it determines the robot control performance to a large extent, this paper aims to improve the accuracy of dynamic model for n-Degree of Freedom (DOF) serial robot.

This paper exploits a combination of the link dynamical system and the friction model to create robot dynamic behaviors. A practical approach to identify the nonlinear joint friction parameters including the slip properties in sliding phase and the stick characteristics in presliding phase is presented. Afterward, an adaptive variable-step moving average method is proposed to effectively reduce the noise impact on the collected data. Furthermore, a radial basis function neural network-based friction estimator for varying loads is trained to compensate the nonlinear effects of load on friction during robot joint moving.

Experiment validations are carried out on all the joints of a 6-DOF industrial robot. The experimental results of joint torque estimation demonstrate that the proposed strategy significantly improves the accuracy of the robot dynamic model, and the prediction effect of the proposed method is better than that of existing methods.

The proposed method extends the robot dynamic model with friction compensation, which includes the nonlinear effects of joint stick motion, joint sliding motion and load attached to the end-effector.

The purpose of this paper is to propose two simple tools for the kinematic characterization of hexapods. The paper also aims to share the experience of converting a popular commercial motion base (Stewart‐Gough platform, hexapod) to an industrial robot for use in heavy duty aerospace manufacturing processes.

The complete workspace of a hexapod is a six‐dimensional entity that is impossible to visualize. Thus, nearly all hexapod manufacturers simply state the extrema of each of the six dimensions, which is very misleading. As a compromise, a special 3D subset of the complete workspace is proposed, an approximation of which can be readily obtained using a computer‐aided design (CAD)/computer‐aided manufacturing (CAM) software suite, such as computer‐aided 3D interactive application (CATIA). While calibration techniques for serial robots are readily available, there is still no generally agreed procedure for calibrating hexapods. The paper proposes a simple calibration method that relies on the use of a laser tracker and requires no programming at all. Instead, the design parameters of the hexapod are directly and individually measured and the few computations involved are performed in a CAD/CAM software such as CATIA.

The conventional octahedral hexapod design has a very limited workspace, though free of singularities. There are important deviations between the actual and the specified kinematic model in a commercial motion base.

A commercial motion base can be used as a precision positioning device with its controller retrofitted with state‐of‐the‐art motion control technology with accurate workspace and geometric characteristics.

A novel geometric approach for obtaining meaningful measures of the workspace is proposed. A novel, systematic procedure for the calibration of a hexapod is outlined. Finally, experimental results are presented and discussed.

Traditional network management tools (NMT) are centralised in nature, as a result of which they are not flexible enough when large control network (e.g. SCADA network) design is desired for. In this paper conventional NMTs have been segmented into components with unified and dedicated functions. Each component has been configured as a client with regard to a central database (i.e. server). The components co‐operate with other components. The work includes the design of flexible NMTs in terms of advanced software architecture for the management of control networks. Three software components (Installation, Configuration and Testing) based on CS architecture and object‐oriented philosophy have been developed. The components are realised with LON™ (Local Operating Network) platform; a proprietary fieldbus system from Echelon, Microsoft’s Visual Basic‐4 platform and Microsoft’s Windows98 operating system. LON Component Architecture Object Server (LCAOS) serves as the network kernel in this design. The configurable components can be used concurrently for the design of control networks.

The purpose of this paper is to describe a calibration method developed to improve the absolute accuracy of a novel three degrees‐of‐freedom planar parallel robot. The robot is designed for the precise alignment of semiconductor wafers and, even though its complete workspace is slightly larger, the accuracy improvements are performed within a target workspace, in which the positions are on a disc of 170 mm in diameter and the orientations are in the range ±17°.

The calibration method makes use of a single optimization model, based on the direct kinematic calibration approach, while the experimental data are collected from two sources. The first source is a measurement arm from FARO Technologies, and the second is a Mitutoyo coordinate measurement machine (CMM). The two sets of calibration results are compared.

Simulation confirmed that the model proposed is not sensitive to measurement noise. An experimental validation on the CMM shows that the absolute accuracy inside the target workspace was improved by reducing the maximum position and orientation errors from 1.432 mm and 0.107°, respectively, to 0.044 mm and 0.009°.

This paper presents a calibration method which makes it possible to accurately identify the actual robot's base frame (base frame calibration), at the same time as identifying and compensating for geometric errors, actuator offsets, and even screw lead errors. The proposed calibration method is applied on a novel planar robot, and its absolute accuracy was found to improve to 0.044 mm.

This study aims to present a vision-guided robotic system design for application in vat photopolymerization additive manufacturing (AM), enabling vat photopolymerization AM hybrid with injection molding process.

In the system, a robot equipped with a camera and a custom-made gripper as well as driven by a visual servoing (VS) controller is expected to perceive objective, handle variation, connect multi-process steps in soft tooling process and realize automation of vat photopolymerization AM. Meanwhile, the vat photopolymerization AM printer is customized in both hardware and software to interact with the robotic system.

By ArUco marker-based vision-guided robotic system, the printing platform can be manipulated in arbitrary initial position quickly and robustly, which constitutes the first step in exploring automation of vat photopolymerization AM hybrid with soft tooling process.

The vision-guided robotic system monitors and controls vat photopolymerization AM process, which has potential for vat photopolymerization AM hybrid with other mass production methods, for instance, injection molding.