Abstract
This paper presents recent results on applying advanced autonomous reasoning capabilities for a planetary rover concept for synthesizing complete command plans that involve a wide assortment of mission requirements. Our solution exploits AI scheduling techniques to manage complex temporal and resource constraints within an integrated power-aware decision-making strategy. The main contributions of this work are the following: (i) we propose a model of the world inspired by the Mars Sample Return (MSR) mission concept, a long-range planetary exploration scenario; (ii) we introduce a MSR-inspired scheduling problem called Power Aware Resource Constrained Mars Rover Scheduling (PARC-MRS), and we present an extension of a well-known constraint-based, resource-driven reasoner that returns rover activity plans as solutions of the PARC-MRS; (iii) we present a benchmark instance generator used to create reproducible PARC-MRS problem sets on the basis of the rover models' specifications contained within the ESA's 3DROV simulator; finally, (iv) we conduct an exhaustive experimentation to report the quality of the generated solutions according to both feasibility and makespan optimization criteria.
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