Behavior Based Control For An Outdoor Crisis Management Robot

Crisis management teams (e.g. fire & rescue services, anti-terrorist units, .. ) are often confronted with dramatic situations where critical decisions have to be made within hard time constraints. In these circumstances, a complete overview of the crisis site is necessary to take correct decisions. However, obtaining such a complete overview of a complex site is not possible in real-life situations when the crisis management teams are confronted with large and complex unknown incident sites. In these situations, the crisis management teams typically concentrate their effort on a primary incident location (e.g. a building on fire, a wreckage, ... ) and only after some time (depending on the manpower and the severity of the incident), they turn their attention towards the larger surroundings, e.g. searching for victims scattered around the incident site. A mobile robotic agent could aid in these circumstances, gaining valuable time by monitoring the area around the primary incident site while the crisis management teams perform their work. However, as the human crisis management teams are in general already overloaded with work and information in any medium or large scale crisis situation, it is essential that such a robotic agent - to be useful - does not require extensive human control (hence it should be semi-autonomous) and it should only report critical information back to the crisis management control center. In the framework of the View-Finder project, such an outdoor mobile robotic platform is being developed. This semi-autonomous agent, shown on figure 1, was equipped with a differential GPS system for accurate geo-registered positioning, and a stereo vision system. In this paper, we discuss the development of the control architecture for this semi-autonomous outdoor mobile robot. The behavior based control paradigm was chosen as a control mechanism due to its flexible nature, allowing the design of complex robot behavior through the integration of multiple relatively simple sensor-actuator relations. Through this control architecture, the robot must be able to search for human victims on an incident site, while navigating semi-autonomously, using stereo vision as a main source of sensor information. The design and development of a control architecture for such a robotic agent raises 3 main questions: