Efficient energy management for autonomous control in rover missions

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 poweraware 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. © 2013 IEEE.

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Díaz, Daniel Armando
Cesta, Amedeo
Oddi, Angelo
Rasconi, Riccardo
R-Moreno, María Dolores
Institute of Electrical and Electronics Engineers,, New York , Stati Uniti d'America
IEEE computational intelligence magazine 8 (2013): 12–24. doi:10.1109/MCI.2013.2279558
info:cnr-pdr/source/autori:Díaz, Daniel Armando; Cesta, Amedeo; Oddi, Angelo; Rasconi, Riccardo; R-Moreno, María Dolores/titolo:Efficient energy management for autonomous control in rover missions/doi:10.1109/MCI.2013.2279558/rivista:IEEE computational in
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