Artificial intelligence technologies for robot assisted urban search and rescue

Structural collapse disasters routinely inspire sympathy not only for victims and their families, but also for heroic rescue personnel who are faced with a tremendously complex hazardous and often frustrating task environment. Military operations and rescue activities in the aftermath of recent earthquakes and bombings indicate a tremendous need for greater access to denied areas within any crisis sire involving collapsed structures. Recent developments in the remote inspection industry show great potential for employment of small robotic micro-rover systems in expanded roles for Urban Search and Rescue. This paper discusses key issues in the application of robotic systems to Urban Search and Rescue (USAR) activities and discusses ongoing development of a knowledge-based system for efficient management of automated search assets. USAR modeling and ''micro-bot'' employment advantages are addressed first, followed by a discussion of numerical method shortcomings in the context of search asset allocation. KNOBSAR is then proposed as an initial expert system prototype designed to interact with various structural collapse simulation packages and provide advice on search asset allocation to specific entry points within a crisis site. KNOBSAR structure and design is then illustrated in terms of micro-bet allocation scenarios to various collapsed structure entry points. The conclusion drawn from literature review experimentation and personal experience is that Al technologies in the form of robotic platforms and decision support tools can have a tremendous impact on overall search efficiency for the USAR community, and represent an important field of study for related military applications.