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The study aims to evaluate the capability of a machine vision camera and software to recognize fasteners for the purpose of assembly verification. This will enable the current assembly verification system to associate torque verfication with a specific fastener.

A small camera is installed at the head of a tool near the socket. The camera is used to capture images surrounding the fastener, and feeding them into machine vision recognition software. By recognizing unique features around the fastener, the fastener can be uniquely identified and therefore verified to be assembled. Additional filtering and multiple frame recognition will improve the reliability of the recognition.

The machine vision technology is found to be adequately reliable in identifying fasteners after tuning key threshold parameters and requiring multiple positively recognized frames. The time to verify can be kept around a fraction of a second to prevent impacting assembly speed.

This experiment was run under simulated assembly line lighting conditions. It also does not explore industrial remote head industrial camera hardware.

By using a remote-mounted camera in combination with electric tools, a reliable assembly verification system can be used to eliminate torque check processes of critical fasteners, thereby reducing the cost of assembly.

Currently, assembly verification is done only using the torque values. In automated assembly line, each process might involve fastening multiple fasteners. Using this system, a new level of assembly verification is achieved by recording the assembled fastener and its associated torque.

Describes an “innovation” in machine vision technology from Cognex, UK called PatMax. This is technology implemented in machine vision software that “understands” images. Describes the benefits of PatMax according to Cognex, UK.

This paper gives an overview of image processing software packages available in the market, their current capabilities and areas of development, and the skills required to use them.

Looks at the move towards integrating robots with highperformance, fully programmable vision systems. Outlines the problems of traditional vision‐aided robotics and the advantage of modern machine vision technology. The latest generation of machine vision systems combine the capabilities of the “C” program system with graphic “point‐and Click” application development environments based on Microsoft Windows: the Checkpoint system. Describes how the Checkpoint vision systems works and the applications of the new vision guided robots. Concludes that the new systems now make it possible for users and system integrators to being the advantages of vision‐guided robotics to general manufacturing.

Describes how a computer disk and storage media company [KAO Infosystems of the USA]uses machine vision technology to maintain the quality of its products by isolating problems and identifying ways of improving the manufacturing process. Emphasises the need to fully define applications and evaluate the technology before introducing a new element to an automation process and stresses the need to integrate the vision system hardware with the plant’s existing manufacturing equipment.

Examines a recently launched integration of smart cameras into industrial robots to make them responsive to a changing environment.

Reviews the capabilities of the vision‐enabled robot, citing installations in Sweden and the UK, then describes the robot and vision programming procedure.

Vision integration opens up a range of new possibilities such as simultaneous product handling and inspection, as well as providing real‐time robot guidance. Standardisation plays an extremely valuable role in building integrated systems from disparate technological elements. Here ActiveX web standards, ethernet connectivity, a standard interchangeable family of cameras and a common controller for a whole range of robots are the keys to the synthesis of a powerful new combination of robot and machine vision.

Draws to the attention of industrial engineers the availability of a family of robots with integrated machine vision.

The purpose of this paper is to provide an insight into the present-day state of bin picking by considering research, technology, products and applications.

Following a short introduction, this first provides examples of recent bin picking research. It then discusses a selection of commercial product developments and applications. Finally, brief conclusions are drawn.

Bin picking has the potential to eliminate repetitive, manual part handling practices in many sectors of the manufacturing and logistics industries. Systems combine robotic gripping and manipulation with machine vision and specialist software and tend to be complex to install and commission. They are produced by robot manufacturers, system integrators, software developers and machine vision specialists and all are constantly developing and improving the technology. These developments are supported by a strong academic research effort, much involving artificial intelligence methods, and while the technology is evolving rapidly, it is yet to reach the point where deployments are routine and widespread.

This provides a timely review of recent bin picking research and commercial developments.

Actimed Laboratories Inc., USA, explain how they are using machine vision technology to develop automated inspection systems for home cholesterol‐screening kits.

The purpose of this paper is to present the development of hardware‐in‐the‐loop simulation (HILS) for visual target tracking of an octorotor unmanned aerial vehicle (UAV) with onboard computer vision.

HILS for visual target tracking of an octorotor UAV is developed by integrating real embedded computer vision hardware and camera to software simulation of the UAV dynamics, flight control and navigation systems run on Simulink. Visualization of the visual target tracking is developed using FlightGear. The computer vision system is used to recognize and track a moving target using feature correlation between captured scene images and object images stored in the database. Features of the captured images are extracted using speed‐up robust feature (SURF) algorithm, and subsequently matched with features extracted from object image using fast library for approximate nearest neighbor (FLANN) algorithm. Kalman filter is applied to predict the position of the moving target on image plane. The integrated HILS environment is developed to allow real‐time testing and evaluation of onboard embedded computer vision for UAV's visual target tracking.

Utilization of HILS is found to be useful in evaluating functionality and performance of the real machine vision software and hardware prior to its operation in a flight test. Integrating computer vision with UAV enables the construction of an unmanned system with the capability of tracking a moving object.

HILS for visual target tracking of UAV described in this paper could be applied in practice to minimize trial and error in various parameters tuning of the machine vision algorithm as well as of the autopilot and navigation system. It also could reduce development costs, in addition to reducing the risk of crashing the UAV in a flight test.

A HILS integrated environment for octorotor UAV's visual target tracking for real‐time testing and evaluation of onboard computer vision is proposed. Another contribution involves implementation of SURF, FLANN, and Kalman filter algorithms on an onboard embedded PC and its integration with navigation and flight control systems which enables the UAV to track a moving object.