Mark Last

One of the main problems in constructing a supervisory robot seems to be the excess of available real-time information. The trend is to make a robot obtaining data from a lot of sensors before it takes a decision. Multiplicity of sensors increases hardware costs and control software complexity. A robot usually needs only a small part of all available information for an efficient performance. The current work will demonstrate a new approach to design of information-efficient supervisory robots. The theory combines the basic concepts of discrete event control extended to stochastic systems with some ideas of information economics. A controlled system behavior is modeled as a sequence of uncontrolled events and robot's decisions. Decisions are based on events observed by robot, and each event needs a sensor to be observed. The model provides a formal statement of the sensors selection problem. A solution procedure of the stated problem will be demonstrated, and some applications of the method to different types of robotic control will be presented.