ON-BOARD AUTONOMY VIA SYMBOLIC MODEL-BASED REASONING ∗

Deep space and remote planetary exploration missions are characterized by severely constrained communication links and often require intervention from Ground to overcome the difficulties encountered during the mission. An adequate Ground control could be compromised due to communication delays and required Ground decision-making time, endangering the system, although safing procedures are strictly adhered to. To meet the needs of future missions and increase their scientific return, space systems will require an increased level of autonomy on-board. We propose a solution to on-board autonomy relying on model-based reasoning. Our approach integrates many important functionalities (such as plan generation, plan execution and monitoring, fault detection identification and recovery, and run-time diagnosis) in a uniform formal framework. The spacecraft is equipped with an Autonomous Reasoning Engine (ARE) structured according to a generic three-layer hybrid autonomy architecture: Deliberative, Executive and Control Layers. The ARE uses a symbolic representation of the controlled platform. Reasoning capabilities are seen as symbolic manipulation of such formal model. We have developed a prototype of the ARE, and we have evaluated it on two case studies inspired by real-world ongoing projects: a planetary rover and an orbiting spacecraft. For each case study, we have used a simulator to characterize the approach in terms of reliability, availability and performances.