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This paper aim to build an information fusion model that can predict the bottom shape of welding groove for better welding quality control. Arc sensor is widely used in seam tracking due to its simplicity and good accessibility, but it heavily relies on the bottom shape of the groove. It is necessary to identify the welding groove bottom state. Therefore, arc sensor information and vision sensing information were fused by the rough set (RS) method to predict the groove state, which will lay the foundation for better welding quality control.

First, a multi-sensor information system was established, which included an arc sensing component and a vision sensing component. For the arc sensing system, the current waveform in each rotating period was obtained and divided into 12 parts to calculate variables representing the variation of arc length. For the vision sensing system, images were obtained by passive vision when the arc was near the groove sidewall. The positions of the sidewall and the arc were calculated to get the weld deviation which was unrelated with the bottom groove state. Second, experimental data were generated by workpiece with various bottom shapes. At last, the RS method was adopted to fuse the arc sensing and the vision information, and a rule-based model with good prediction ability was obtained.

By fusing arc sensing and vision sensing information, an RS-based model was built to predict the welding groove state.

The RS modeling method was used to fuse arc sensing information and vision sensing information to build a model that predicts groove bottom state. The arc sensing information represented the arc length variation, while the vision sensing information contains the seam deviation which was unrelated with the bottom groove state. The RS model gives satisfactory prediction results and can be applied to weld quality control.

Vision, audition, olfactory, tactile and taste are five important senses that human uses to interact with the real world. As facing more and more complex environments, a sensing system is essential for intelligent robots with various types of sensors. To mimic human-like abilities, sensors similar to human perception capabilities are indispensable. However, most research only concentrated on analyzing literature on single-modal sensors and their robotics application.

This study presents a systematic review of five bioinspired senses, especially considering a brief introduction of multimodal sensing applications and predicting current trends and future directions of this field, which may have continuous enlightenments.

This review shows that bioinspired sensors can enable robots to better understand the environment, and multiple sensor combinations can support the robot’s ability to behave intelligently.

The review starts with a brief survey of the biological sensing mechanisms of the five senses, which are followed by their bioinspired electronic counterparts. Their applications in the robots are then reviewed as another emphasis, covering the main application scopes of localization and navigation, objection identification, dexterous manipulation, compliant interaction and so on. Finally, the trends, difficulties and challenges of this research were discussed to help guide future research on intelligent robot sensors.

The purpose of this paper is to review some of the latest in new vision sensor technologies as well as other innovative sensor products being developed and reaching the market.

This study is a review of published information and papers on research as well as contact and discussions with researchers and suppliers in this field at the Vision Show and the Ceramics Show.

Microelectronics and electrochemical technologies have been a major factor in technology advancements of sensors for a wide range of applications. Vision sensors have become very important, as applications such as drone aircraft and driver less cars have dramatically grown. Technology has advanced and so sensors are becoming smarter, are smaller, offer better resolution, are much more sensitive than in the past and address previously unserved applications.

Readers may be very excited to learn of the many advances in vision and other technologies which are coming to the sensor field. Such sensors are addressing new applications that were not previously possible which are now being served.

Scientists at Linkoping Institute of Technology assess the state of tactile sensing and offer some of their own ideas.

This paper aims to develop a performing approach for telerobotic arc welding in an unstructured environment.

A teleteaching approach is presented for an arc welding telerobotic system in an unstructured environment. Improved laser vision sensor enhances the precision of teleteaching welding seam. Stereoscopic vision display system is developed to provide the perception information of remote environment that increased the dexterity of the teleteaching process. Operator interacts with the system by welding multi‐modal human‐machine interface, which integrated the teleteaching operation window, status display window and space mouse.

The sensor‐based teleteaching approach, which integrated laser vision sensing and stereoscopic vision display, can perform arc welding of most welding seam trajectory in an unstructured environment. The approach releases the payload of human operator and improves adaptability of the arc welding system.

The paper provides the remote welding telerobotic approach that is gentle to most unstructured environments.

The sensor‐based teleteaching approach provides the capability of a telerobotic system used in remote welding field, which can shorten the incident response time and maintenance period of nuclear plants, space and underwater.

This paper introduces the sensor‐based teleteaching concept and performing procedure to be used for remote telerobotic arc welding.

At a time when there is a seeming influx of imports the US is turning increasingly to the exploitation of its advanced technology and manufacturing research. One key area is that of sensor research which is likely to have major implications.

Looks at the new generation of MetaTorch sensor systems which cost a fraction of the original sensors produced ten years ago as well as being smaller in size, lighter and having greater flexibility. Describes the basic principles of optical seam sensing using state of the art charge‐coupled devices [CCDs], laser diodes and a microprocessor which controls and monitors the major functions. New vision software interfaces to most industrial robots and welding machines for both normal arc‐welding and laser welding. Concludes that previously the widespread use of adaptive vision sensing has been prevented by its relatively high price; the new price structure is likely to have an impact on the use of intelligent sensing systems for many industrial applications.

The aim of the paper is to explore leadership behavior as a process of sensing through the connection of various aspects of the self to external dynamics.

The paper is based on an in-depth study of an international technology firm where 36 managers were interviewed. Content analysis was used to interpret the data.

If leaders learn to be more authentic in the way they think, feel, and act, they will be more connected to who they are and what they stand for in the face of uncertainty.

Leaders should embrace adversity as an opportunity to connect more deeply to their inner senses by breaking away from their comfort zones and acting courageously through their decisions.

The paper offers a different understanding of adaptive leadership from the sensing perspective. The firsthand experience of managers challenged by the Covid-19 pandemic offers fresh insights into the study of leadership behavior.

Outcome with a novel methodology for online recognition and classification of pieces in robotic assembly tasks and its application into an intelligent manufacturing cell.

The performance of industrial robots working in unstructured environments can be improved using visual perception and learning techniques. The object recognition is accomplished using an artificial neural network (ANN) architecture which receives a descriptive vector called CFD&POSE as the input. Experimental results were done within a manufacturing cell and assembly parts.

Find this vector represents an innovative methodology for classification and identification of pieces in robotic tasks, obtaining fast recognition and pose estimation information in real time. The vector compresses 3D object data from assembly parts and it is invariant to scale, rotation and orientation, and it also supports a wide range of illumination levels.

Provides vision guidance in assembly tasks, current work addresses the use of ANN's for assembly and object recognition separately, future work is oriented to use the same neural controller for all different sensorial modes.

Intelligent manufacturing cells developed with multimodal sensor capabilities, might use this methodology for future industrial applications including robotics fixtureless assembly. The approach in combination with the fast learning capability of ART networks indicates the suitability for industrial robot applications as it is demonstrated through experimental results.

This paper introduces a novel method which uses collections of 2D images to obtain a very fast feature data – ”current frame descriptor vector” – of an object by using image projections and canonical forms geometry grouping for invariant object recognition.

This year's AUTOFACT WEST moved to California where, through its close attention to assembly, vision and tactile sensing, the exhibition and conference reflected advanced systems activity in this region of the US.