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Traditional gas tungsten arc welding (GTAW) and GTAW-based wire and arc additive manufacturing (WAAM) are notably different. These differences are crucial to the process stability and surface quality in GTAW WAAM. This paper addresses special characteristics and the process control method of GTAW WAAM. The purpose of this paper is to improve the process stability with sensor information fusion in omnidirectional GTAW WAAM process.

A wire feed strategy is proposed to achieve an omnidirectional GTAW WAAM process. Thus, a model of welding voltage with welding current and arc length is established. An automatic control system fit to the entire GTAW WAAM process is established using both welding voltage and welding current. The effect of several types of commonly used controllers is examined. To assess the validity of this system, an arc length step experiment, various wire feed speed experiments and a square sample experiment were performed.

The research findings show that the resented wire feed strategy and arc length control system can effectively guarantee the stability of the GTAW WAAM process.

This paper tries to make a foundation work to achieve omnidirectional welding and process stability of GTAW WAAM through wire feed geometry analysis and sensor information fusion control model. The proposed wire feed strategy is implementable and practical, and a novel sensor fusion control method has been developed in the study for varying current GTAW WAAM process.

Electron beam-based additive manufacturing (EBAM) is an emerging technology to produce metal parts layer-by-layer. The purpose of this paper is to systematically address the research and development carried out for this technology, up till now.

This paper identifies several aspects of research and development in EBAM.

Electron beam has several unique advantages such as high scanning speed, energy efficiency, versatility for several materials and better part integrity because of a vacuum working environment.

This paper provides information on different aspects of EBAM with the current status and future scope.

Surface forming control of welding bead is the fundamental study in automated welding. Considering that the vision sensing system cannot extract the height information of weld pool in pulsed GTAW process, so this paper designed a set of automatic measurement and control technology to achieve real-time arc height control via audio sensing system. The paper aims to discuss these issues.

The experiment system is based on GTAW welding with acoustic sensor and signal conditioner. A combination denoising method was used to reduce the environmental noise and pulse interference noise. After extracting features of acoustic signal, the relationship between arc height and arc sound pressure was established by linear fitting. Then in order to improve the prediction accuracy of that model, the piecewise linear fitting method was proposed. Finally, arc height linear model of arc sound signal and arc height is divided into two parts and built in two different arc height conditions, which are arc height 3-4 and 4-5-6 mm.

The combination denoising method was proved to have great effect on reducing the environmental noise and pulse interference noise. The experimental results showed that the prediction accuracy of linear model was not stable in different arc height changing state, like 3-4 and 4-5-6 mm. The maximum error was 0.635588 mm. And the average error of linear model was about 0.580487 mm, and the arc sound signal was accurately enough to meet the requirement for real-time control of arc height in pulse GTAW.

This paper tries to make a foundation work to achieve controlling of depth of welding pool through arc sound signal, then the welding quality control. So a new idea of arc height control based on automatic measuring and processing system through arc sound signal was proposed. A new way to remove environmental noise and pulse interference noise was proposed. The results of this thesis had proved that arc sound signal was an effective features and precisely enough for online arc height monitoring during pulsed GTAW.

The purpose of this paper is to study the relationship between arc sound signal and arc height through arc sound features of GTAW welding, which is aimed at laying foundation work for monitoring the welding penetration and quality by using the arc sound signal in the future.

The experiment system is based on GTAW welding with acoustic sensor and signal conditioner on it. The arc sound signal was first processed by wavelet analysis and wavelet packet analysis designed in this research. Then the features of arc sound signal were extracted in time domain, frequency domain, for example, short‐term energy, AMDF, mean strength, log energy, dynamic variation intensity, short‐term zero rate and the frequency features of DCT coefficient, also the wavelet packet coefficient. Finally, a ANN (artificial neural networks) prediction model was built up to recognize different arc height through arc sound signal.

The statistic features and DCT coefficient can be absolutely used in arc sound signal processing; and these features of arc sound signal can accurately react the modification of arc height during the GTAW welding process.

This paper tries to make a foundation work to achieve monitoring arc length through arc sound signal. A new way to remove high frequency noise of arc sound signal is produced. It proposes some effective statistic features and a new way of frequency analysis to build the prediction model.

Penetration state is one of the most important factors for judging the quality of a gas tungsten arc welding (GTAW) joint. The purpose of this paper is to identify and classify the penetration state and welding quality through the features of arc sound signal during robotic GTAW process.

This paper tried to make a foundation work to achieve on‐line monitoring of penetration state to weld pool through arc sound signal. The statistic features of arc sound under different penetration states like partial penetration, full penetration and excessive penetration were extracted and analysed, and wavelet packet analysis was used to extract frequency energy at different frequency bands. The prediction models were established by artificial neural networks based on different features combination.

The experiment results demonstrated that each feature in time and frequency domain could react the penetration behaviour, arc sound in different frequency band had different performance at different penetration states and the prediction model established by 23 features in time domain and frequency domain got the best prediction effect to recognize different penetration states and welding quality through arc sound signal.

This paper tried to make a foundation work to achieve identifying penetration state and welding quality through the features of arc sound signal during robotic GTAW process. A total of 23 features in time domain and frequency domain were extracted at different penetration states. And energy at different frequency bands was proved to be an effective factor for identifying different penetration states. Finally, a prediction model built by 23 features was proved to have the best prediction effect of welding quality.

Building inspection tasks usually involve working at life‐threatening height, especially for high‐rise buildings. The purpose of this paper is to introduce two service robots which are designed for high‐rise building inspection applications.

The first service robot, equipped with independent‐climbing capability, is applied to gas pipe inspection. The robot requires very little setup time and is suitable for some small inspection tasks. The second, which shares some similarities of industrial gondolas, is applied to check the health conditions of tile‐walls of high‐rise buildings. This robot requires more initial setup time but can provide faster inspection operations. In addition, it can be programmed to carry out the inspection task automatically. So, it is more suitable for large‐scale inspection tasks.

For tile‐wall inspection, a fast, low‐cost and effective non‐destructive testing technique based on impact acoustic method has been developed for the robot.

Both prototypes have been applied to housing estates for evaluation purposes.

The purpose of this paper is to present a low-cost, highly mobile system for performing street-level imaging. Street-level imaging and geo-location-based services are rapidly growing in both popularity and coverage. Google Street View and Bing StreetSide are two of the free, online services which allow users to search location-based information on interactive maps. In addition, these services also provide software developers and researchers a rich source of street-level images for different purposes – from identifying traffic routes to augmented reality applications. Currently, coverage for Street View and StreetSide is limited to more affluent Western countries with sparse coverage throughout south-east Asia and Africa. In this paper, we present a low-cost system to perform street-level imaging targeted towards the congested, motorcycle-dominant south-east Asian countries. The proposed system uses a catadioptric imaging system to capture 360-degree panoramic images which are geo-located using an on-board GPS. The system is mounted on the back of a motorcycle to provide maximum mobility and access to narrow roads. An innovative backwards remapping technique for flattening the images is discussed along with some results from the first 150 km which have been captured from Southern Vietnam.

The design was a low-cost prototype design using low-cost off-the-shelf hardware with custom software and assembly to facilitate functionality.

The system was shown to work well as a low-cost omnidirectional mapping solution targeted toward sea-of-motorbike road conditions.

Some of the pictures returned by the system were unclear. These could be improved by having artificial lighting (currently only ambient light is used), a gyroscope-stabilised imaging platform and a higher resolution camera.

This paper discusses a design which facilitates low-cost, street-level imaging for a sea-of-motorcycle environment. The system uses a catadioptric imaging approach to give a wide field of view without excessive image storage requirements using dozens of cameras.

The Industry 4.0 initiative – with its ultimate objective of revolutionizing the supply-chain – putted more emphasis on smart and autonomous systems, creating new opportunities to add flexibility and agility to automatic manufacturing systems. These systems are designed to free people from monotonous and repetitive tasks, enabling them to concentrate in knowledge-based jobs. One of these repetitive functions is the order-picking task which consists of collecting parts from storage (warehouse) and distributing them among the ordering stations. An order-picking system can also pick finished parts from working stations to take them to the warehouse. The purpose of this paper is to present a simplified model of a robotic order-picking system, i.e. a mobile manipulator composed by an automated guided vehicle (AGV), a collaborative robot (cobot) and a robotic hand.

Details about its implementation are also presented. The AGV is needed to safely navigate inside the factory infrastructure, namely, between the warehouse and the working stations located in the shop-floor or elsewhere. For that purpose, an ActiveONE AGV, from Active Space Automation, was selected. The collaborative robot manipulator is used to move parts from/into the mobile platform (feeding the working stations and removing parts for the warehouse). A cobot from Kassow Robots was selected (model KR 810), kindly supplied by partner companies Roboplan (Portugal) and Kassow Robotics (Denmark). An Arduino MKR1000 board was also used to interconnect the user interface, the AGV and the collaborative robot. The graphical user interface was developed in C# using the Microsoft Visual Studio 2019 IDE, taking advantage of this experience in this type of language and programming environment.

The resulting prototype was fully demonstrated in the partner company warehouse (Active Space Automation) and constitutes a possible order-picking solution, which is ready to be integrated into advanced solutions for the factories of the future.

A solution to fully automate the order-picking task at an industrial shop-floor was presented and fully demonstrated. The objective was to design a system that could be easy to use, to adapt to different applications and that could be a basic infrastructure for advanced order-picking systems. The system proved to work very well, executing all the features required for an order-picking system working in an Industry 4.0 scenario where humans and machines must act as co-workers. Although all the system design objectives were accomplished, there are still opportunities to improve and add features to the presented solution. In terms of improvements, a different robotic hand will be used in the final setup, depending on the type of objects that are being required to move. The amount of equipment that is located on-board of the AGV can be significantly reduced, freeing space and lowering the weight that the AGV carries. For example, the controlling computer can be substituted by a single-board-computer without any advantage. Also, the cobot should be equipped with a wrist camera to identify objects and landmark. This would allow the cobot to fully identify the position and orientation of the objects to pick and drop. The wrist camera should also use bin-picking software to fully identify the shape of the objects to pick and also their relative position (if they are randomly located in a box, for example). These features are easy to add to the developed mobile manipulator, as there are a few vision systems in the market (some that integrate with the selected cobot) that can be easily integrated in the solution. Finally, this paper reports a development effort that neglected, for practical reasons, all issues related with certification, safety, training, etc. A future follow-up paper, reporting a practical use-case implementation, will properly address those practical and operational issues.

Nuclear waste tanks need to be cut into pieces before they can be safely disposed of, but the cutting process produces a large amount of aerosols with radiation, which is very harmful to the health of the operator. The purpose of this paper is to establish an intelligent strategy for an integrated robot designed for measurement and cutting, which can accurately identify and cut unknown nuclear waste tanks and realize autonomous precise processing.

A robot system integrating point cloud measurement and plasma cutting is designed in this paper. First, accurate calibration methods for the robot, tool and hand-eye system are established. Second, for eliminating the extremely scattered point cloud caused by metal surface refraction, an omnidirectional octree data structure with 26 vectors is proposed to extract the point cloud model more accurately. Then, a minimum bounding box is calculated for limiting the local area to be cut, the local three-dimensional shape of the nuclear tank is fitted within the bounding box, in which the cutting trajectories and normal vectors are planned accurately.

The cutting precision is verified by changing the tool into a dial indicator in the simulation and the experiment process. The octree data structure with omnidirectional pointing vectors can effectively improve the filtering accuracy of the scattered point cloud. The point cloud filter algorithm combined with the structure calibration methods for the integrated measurement and processing system can ensure the final machining accuracy of the robot.

Aiming at the problems of large measurement noise interference, complex transformations between coordinate systems and difficult accuracy guarantee, this paper proposes structure calibration, point cloud filtering and point cloud-based planning algorithm, which can greatly improve the reliability and accuracy of the system. Simulation and experiment verify the final cutting accuracy of the whole system.