Probabilistic online-generated monitoring models for mobile robot navigation using modified Petri net

This paper presents a generic hybrid online monitoring approach allowing the detection of inconsistencies in the navigation of autonomous mobile robots. The originalities of this work are (i) the association of classic state estimation based on particle filter to a special class of Petri net in order to deliver an estimation of the next robot state (position) as well as the environment state (graph nodes) and to use both information to distinguish between external noise influences, internals component faults or global behaviour inconsistency (ii) the integration of the geometrical and the logical environment representation into the monitor model in order to generate online the corresponding navigation monitoring model for the present mission trajectory while the system is running. The model takes simultaneously into account the uncertainty of the robot and of the environment through a unified modeling of both. To show the feasibility of the approach we apply it to an Intelligent Wheelchair (IWC) as a special type of autonomous mobile robot.