Associative memory for modified Petri-net based monitoring of mobile robot navigation

This paper presents an extension of a generic hybrid online monitoring approach for the navigation of autonomous mobile robots through an associative memory. Due to this extension, when a fault occurs the navigation process does not have to stop running in order to keep the system in a safe state and to ensure a high dependability. The associative memory enables the robot to remember the previously traversed paths inside a known environment and to learn new, unknown paths on-line. This long-term memory makes the monitor more dependable and more robust with respect to the fault detection, diagnosis and handling. To show the feasibility of the approach we apply it on an example of a mobile robot application.