| Sommario: | This paper introduces a software prototype called ARPHA for on-board diagnosis, prognosis and recovery. e goal is to allow the design of an innovative on-board FDIR (Fault Detection, Identification and Recovery) process for autonomous systems, able to deal with uncertain system/environment interactions, uncertain dynamic system evolution, partial observability and detection of recovery policies taking into account imminent failures. We propose to base the inference engine of ARPHA on Dynamic Probabilistic Graphical Models suitable to reason about system evolution with control actions, over a finite time horizon. e model needed by ARPHA is derived from standard dependability modeling, exploiting an extension of the Dynamic Fault Tree language, called EDFT. We finally discuss the software architecture of ARPHA, where on-board FDIR is implemented and we provide some preliminary results on simulation scenarios for Mars rover activities. |
