History checking of temporal fuzzy logic formulas for monitoring behavior-based mobile robots

Behavior-based robot control systems have shown remarkable success for controlling robots evolving in real world environments. However, they can fail in different manners due to their distributed control and their local decision making. In this case, monitoring can be used to detect failures and help to recover from them. In this work, we present an approach for specifying monitoring knowledge and a method for using this knowledge to detect failures. In particular we show how temporal fuzzy logic can be used to represent monitoring knowledge and then utilized to effectively detect runtime failures. New semantics are introduced to take into consideration uncertainty and noisy information. There are numbers of advantages to our approach including a declarative semantics for the monitoring knowledge and an independence of this knowledge from the implementation details of the control system. Moreover we show how our system can deal effectively with noisy information and sensor readings. Experiments with two real world robots and the simulator are used to illustrate failure examples and the benefits of failure detection and noise elimination.