An Integrated System for Multi-Rover Scientific Exploration

Tara Estlin, Alexander Gray, Tobias Mann, Gregg Rabideau, Rebecca Castaño, Steve Chien, and Eric Mjolsness, Jet Propulsion Laboratory

This paper describes an integrated system for coordinating multiple rover behavior with the overall goal of collecting planetary surface data. The Multi-Rover Science Exploration system integrates 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 planning and scheduling system is employed to generate rover plans for achieving science goals and to coordinate activities among rovers. We describe each of these components and discuss some of the key integration issues that arose during development and influenced both system design and performance. Theme: (AI Integrated Systms) New syntheses that bring together work in sub-disciplines of AI toward the creation of complete systems (e.g. architectural frameworks, learning and planning, etc.)

This page is copyrighted by AAAI. All rights reserved. Your use of this site constitutes acceptance of all of AAAI's terms and conditions and privacy policy.