An overview of the self-organizing map algorithm, on which the papers in this issue are based, is presented in this article.

The Self-Organizing Map

Intelligent Manufacturing in the Context of Industry 4.0: A Review

Deep neural networks: A promising tool for fault characteristic mining and intelligent diagnosis of rotating machinery with massive data

Machinery health prognostics: A systematic review from data acquisition to RUL prediction

https://escholarship.org/content/qt5ns8r3fs/qt5ns8r3fs.pdf?t=pihyfp

Remaining useful life estimation in prognostics using deep convolution neural networks

Prognostics and health management design for rotary machinery systems—Reviews, methodology and applications

The purpose of this paper is to describe the design and implementation of an unmanned ground vehicle, called the Bearcat III, named after the University of Cincinnati mascot. The Bearcat III is an electric powered, three-wheeled vehicle that was designed for the Intelligent Ground Vehicle Competition and has been tested in the contest for 5 years. The dynamic model, control system, and design of the sensory systems are described. For the autonomous challenge line following, obstacle detection and pothole avoidance are required. Line following is accomplished with a dual camera system and video tracker. Obstacle detection is accomplished with either a rotating ultrasound or laser scanner. Pothole detection is implemented with a video frame grabber. For the navigation challenge waypoint following and obstacle detection are required. The waypoint navigation is implemented with a global positioning system. The Bearcat III has provided an educational test bed for not only the contest requirements but also other studies in developing artificial intelligence algorithms such as adaptive learning, creative control, automatic calibration, and internet-based control. The significance of this effort is in helping engineering and technology students understand the transition from theory to practice. © 2004 Wiley Periodicals, Inc.

/pdf/design-of-an-unmanned-ground-vehicle-bearcat-iii-theory-and-ffkney2kej.pdf

Design of an unmanned ground vehicle, bearcat III, theory and practice

After proving to be an efficient tool for improving quality, productivity, and competitiveness of manufacturing organizations, robots now expand to service organizations, offices, and even homes. Global competition and the tendency to reduce production cost and increase efficiency creates new applications for robots that stationary robots can't perform. These new applications require the robots to move and perform certain activities at the same time. The availability and low cost of faster processors, better programming, and the use of new hardware allow robot designers to build more accurate, faster, and even safer robots. For example, Egemin Automation has been selling mobile robots for a number of years, available as a specialized unit that delivers mail within a large building to warehouse automation systems. Currently, mobile robots are expanding outside the confines of buildings and into rugged terrain, as well as familiar environments like schools and city streets (Wong, 2005). The problem addressed in this paper is to describe the theory and architecture of robust learning for mobile robots and to illustrate the theory for designing intelligent mobile robots for a wide variety of applications anywhere in the world including complex and uncertain environments. The proposed architecture for machine learning is also based on the perceptual creative controller for an intelligent robot that uses a multi- modal adaptive critic for performing learning in an unsupervised situation but can also be trained for tasks in another mode and then is permitted to operate autonomously. The robust nature is derived from the automatic changing of modes based on internal measurements of error at appropriate locations in the controller.

/pdf/mobile-robotics-moving-intelligence-2h6viacoxa.pdf

Mobile Robotics, Moving Intelligence

An autonomous robot must be able to sense its environment and react appropriately in a variable environment. The University of Cincinnati Robot team is actively involved in building a small, unmanned, autonomously guided vehicle for the International Ground Robotics Contest organized by Association for Unmanned Vehicle Systems International (AUVSI) each year. The unmanned vehicle is supposed to follow an obstacle course bounded by two white/yellow lines,
which are four inches thick and 10 feet apart. The navigation system for one of the University of Cincinnati’s designs, Bearcat, uses 2 CCD cameras and an image-tracking device for the front end processing of the image captured by the cameras. The three dimensional world co-ordinates were reduced to two dimensional image coordinates as a result of the transformations taking place from the ground plane to the image plane. A novel automatic calibration system was designed to transform the image co-ordinates back to world co-ordinates for navigation purposes. The purpose of this paper is to simplify this tedious calibration using an artificial neural network. Image processing is used to automatically detect calibration points. Then a back projection neural algorithm is used to learn the relationships between the image coordinates and three-dimensional coordinates. This transformation is the main focus of this study. Using these
algorithms, the robot built with this design is able to track and follow the lines successfully.

https://ceas.uc.edu/content/dam/ceas/documents/UC%20Center%20for%20Robotics%20Research/robpub43.pdf

Automatic calibration and neural networks for robot guidance

Secure remote access with inter-operatability for operating a robot can be successfully achieved using the web services provided in the .NET framework. The complete design of the machine discussed in this paper is made on the .NET framework. The server which operates the robot is configured to IIS. The algorithm for obstacle detection is coded on a different server using the .NET framework. By using web services, the robot can be accessed by other servers. These web services are consumed by the server on which the robot executes. A proxy is created on this server. The whole control is given in the form of a series of web pages which can be accessed by any web browser. However in order to input parameters and control the robot, authentication is required. The user provides authentication credentials which are matched with the existing information on the data base. After authentication, the user proceeds further to control the robot. The security and reliability of remote access is provided by the components that come with the web services namely, SOAP, WSDL and Proxy.

https://ceas.uc.edu/content/dam/ceas/documents/UC%20Center%20for%20Robotics%20Research/robpub41.pdf

Masoud Ghaffari

Papers

Prognostics and health management design for rotary machinery systems—Reviews, methodology and applications

Design of an unmanned ground vehicle, bearcat III, theory and practice

Mobile Robotics, Moving Intelligence

Automatic calibration and neural networks for robot guidance

Internet-based control for the intelligent unmanned ground vehicle: Bearcat Cub