Rustam Stolkin is a Research Assistant Professor at the Center for Maritime Systems, Stevens Institute of Technology. He received a MEng degree in Engineering from Oxford University and a PhD in Robot Vision from London University. His research interests include robotics, computer vision, machine intelligence and sensor Systems.
This book reports recent advances in the use of pattern recognition techniques for computer and robot vision. The sciences of pattern recognition and computational vision have been inextricably intertwined since their early days, some four decades ago with the emergence of fast digital computing. All computer vision techniques could be regarded as a form of pattern recognition, in the broadest sense of the term. Conversely, if one looks through the contents of a typical international pattern recognition conference proceedings, it appears that the large majority (perhaps 70-80%) of all pattern recognition papers are concerned with the analysis of images. In particular, these sciences overlap in areas of low level vision such as segmentation, edge detection and other kinds of feature extraction and region identification, which are the focus of this book.
Go to the bookDespite enormous remote handling requirements, remarkably very few robots are being used by the nuclear industry. Most of the remote handling tasks are still performed manually, using conventional mechanical master‐slave devices. The few robotic manipulators deployed are directly tele‐operated in rudimentary ways, with almost no autonomy or even a pre‐programmed motion. In addition, majority of these robots are under‐sensored (i.e. with no proprioception), which prevents them to use for automatic tasks. In this context, primarily this chapter discusses the human operator performance in accomplishing heavy‐duty remote handling tasks in hazardous environments such as nuclear decommissioning. Multiple factors are evaluated to analyse the human operators’ performance and workload. Also, direct human tele‐operation is compared against human‐supervised semi‐autonomous control exploiting computer vision. Secondarily, a vision‐guided solution towards enabling advanced control and automating the under‐sensored robots is presented. Maintaining the coherence with real nuclear scenario, the experiments are conducted in the lab environment and results are discussed.
Part of the book: Robots Operating in Hazardous Environments