Tara Estlin, Rebecca Castaño, Ashley Davies, Darren Mutz, Gregg Rabideau, Steve Chien, and Eric Mjolsness, Jet Propulsion Laboratory, California Institute of Technology, USA
This paper describes an integrated system for coordinating multiple rover behavior with the overall goal of collecting planetary surface data. The Multi- Rover Integrated Science Understanding System combines concepts from machine learning with planning and scheduling to perform autonomous scientific exploration by cooperating rovers. The integrated system utilizes a novel machine-learning clustering component to analyze science data and direct new science activities. A distributed planning and scheduling system is employed to generate rover plans for achieving science goals, to coordinate activities among rovers, and to replan when necessary. We describe each of these components and describe how they are integrated with a planetary environment simulation.