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This paper aims to study six degrees-of-freedom (6DOF) pose measurement of reflective metal casts by machine vision, analyze the problems existing in the positioning of metal casts by stereo vision sensor in unstructured environment and put forward the visual positioning and grasping strategy that can be used in industrial robot cell.

A multikeypoints detection network Binocular Attention Hourglass Net is constructed, which can complete the two-dimensional positioning of the left and right cameras of the stereo vision system at the same time and provide reconstruction information for three-dimensional pose measurement. Generate adversarial networks is introduced to enhance the image of local feature area of object surface, and the three-dimensional pose measurement of object is completed by combining RANSAC ellipse fitting algorithm and triangulation method.

The proposed method realizes the high-precision 6DOF positioning and grasping of reflective metal casts by industrial robots; it has been applied in many fields and solves the problem of difficult visual measurement of reflective casts. The experimental results show that the system exhibits superior recognition performance, which meets the requirements of the grasping task.

Because of the chosen research approach, the research results may lack generalizability. The proposed method is more suitable for objects with plane positioning features.

This paper realizes the 6DOF pose measurement of reflective casts by vision system, and solves the problem of positioning and grasping such objects by industrial robot.

ASEA, the Swedish robot builder, has introduced a robust robot vision system which is easy to program by the person on the shopfloor. John Mortimer reports.

Robot learning ‐ be it unsupervised, supervised or self‐supervised ‐ is one method of dealing with noisy, inconsistent, or contradictory data that has proven useful in mobile robotics. In all but the simplest cases of robot learning, raw sensor data cannot be used directly as input to the learning process. Instead, some “meaningful” preprocessing has to be applied to the raw data, before the learning controller can use the sensory perceptions as input. In this paper, two instances of supervised and unsupervised robot learning experiments, using vision input are presented. The vision sensor signal preprocessing necessary to achieve successful learning is also discussed.

The purpose of this paper is to present a visual detection approach to predict the poses of target objects placed in arbitrary positions before completing the corresponding tasks in mobile robotic manufacturing systems.

A hybrid visual detection approach that combines monocular vision and laser ranging is proposed based on an eye-in-hand vision system. The laser displacement sensor is adopted to achieve normal alignment for an arbitrary plane and obtain depth information. The monocular camera measures the two-dimensional image information. In addition, a robot hand-eye relationship calibration method is presented in this paper.

First, a hybrid visual detection approach for mobile robotic manufacturing systems is proposed. This detection approach is based on an eye-in-hand vision system consisting of one monocular camera and three laser displacement sensors and it can achieve normal alignment for an arbitrary plane and spatial positioning of the workpiece. Second, based on this vision system, a robot hand-eye relationship calibration method is presented and it was successfully applied to a mobile robotic manufacturing system designed by the authors’ team. As a result, the relationship between the workpiece coordinate system and the end-effector coordinate system could be established accurately.

This approach can quickly and accurately establish the relationship between the coordinate system of the workpiece and that of the end-effector. The normal alignment accuracy of the hand-eye vision system was less than 0.5° and the spatial positioning accuracy could reach 0.5 mm.

This approach can achieve normal alignment for arbitrary planes and spatial positioning of the workpiece and it can quickly establish the pose relationship between the workpiece and end-effector coordinate systems. Moreover, the proposed approach can significantly improve the work efficiency, flexibility and intelligence of mobile robotic manufacturing systems.

This paper aims to review the use of imaging technologies in robotics, with an emphasis on inspection applications and the control of autonomous robots.

Following a brief introduction, this paper first considers vision‐based robotic inspection systems and highlights a selection of recent applications. Second, it considers the use of vision in autonomous robot navigation and discusses some of the challenges and recent developments.

This shows that developments in machine vision have led to vision systems being used in a diversity of component‐level and in‐service robotic inspection tasks. It also illustrates that vision systems have a key role to play in the emerging generation of autonomous, mobile robots.

This paper provides a review of recent developments in vision‐based robotic inspection and autonomous, mobile robot navigation.

The positioning and grasping of large-size objects have always had problems of low positioning accuracy, slow grasping speed and high application cost compared with ordinary small parts tasks. This paper aims to propose and implement a binocular vision-guided grasping system for large-size object with industrial robot.

To guide the industrial robot to grasp the object with high position and pose accuracy, this study measures the pose of the object by extracting and reconstructing three non-collinear feature points on it. To improve the precision and the robustness of the pose measuring, a coarse-to-fine positioning strategy is proposed. First, a coarse but stable feature is chosen to locate the object in the image and provide initial regions for the fine features. Second, three circular holes are chosen to be the fine features whose centers are extracted with a robust ellipse fitting strategy and thus determine the precise pose and position of the object.

Experimental results show that the proposed system has achieved high robustness and high positioning accuracy of −1 mm and pose accuracy of −0.5 degree.

It is a high accuracy method that can be used for industrial robot vision-guided and grasp location.

The first production implementation of the General Motors Consight vision system at the St. Catherines, Ontario, foundry is successfully sorting up to six different castings at up to 1,400 an hour from a belt conveyor using three industrial robots in a harsh manufacturing environment.

The purpose of this study is to design, program and implement an intelligent robot for shelf-reading. an essential task in library maintenance is shelf-reading, which refers to the process of checking the disciplines of books based on their call numbers to ensure that they are correctly shelved. Shelf-reading is a routine yet challenging task for librarians, as it involves controlling call numbers on the scale of thousands of books promptly.

Leveraging the strength of autonomous robots in handling repetitive tasks, this paper introduces a novel vision-based shelf-reader robot, called \emph{Pars} and demonstrate its effectiveness in accomplishing shelf-reading tasks. Also, this paper proposes a novel supervised approach to power the vision system of \emph{Pars}, allowing it to handle motion blur on images captured while it moves. An approach based on Faster R-CNN is also incorporated into the vision system, allowing the robot to efficiently detect the region of interest for retrieving a book’s information.

This paper evaluated the robot’s performance in a library with $120,000 books and discovered problems such as missing and misplaced books. Besides, this paper introduces a new challenging data set of blurred barcodes free publicly available for similar research studies.

The robot is equipped with six parallel cameras, which enable it to check books and decide moving paths. Through its vision-based system, it is also capable of routing and tracking paths between bookcases in a library and it can also turn around bends. Moreover, \emph{Pars} addresses the blurred barcodes, which may appear because of its motion.

This paper's aim is to assess the practical advances resulting from progress in artificial intelligence affecting vision‐equipped robots.

A short history of artificial intelligence applied to robotic hand‐eye coordination is followed by a description of some of the milestones in pattern recognition, interfacing, operating systems and programming paradigms. Finally, a range of current‐day practical applications is given, from industrial to student project.

The paper finds that advances in the speed and robustness of pattern recognition algorithms have been very important in the development of robots that adapt to randomly positioned workpieces. Event‐triggered object oriented programming, wide address buses, smart cameras, ethernet and other standard cables and communications formats, and the ever‐increasing power of computers are also of great practical importance.

The paper contrasts the current state of robotic vision with that 20 years ago.

A case study by IBM of machine vision implementation in the robotic assembly area of the Automated Logistic Production System used in manufacturing computers.