Enabling autonomous rover science through dynamic planning and scheduling

Abstract

With each new rover mission to Mars, rovers are traveling significantly longer distances. This distance increase allows not only the collection of more science data, but enables a number of new and different science collection opportunities. Current mission operations, such as that on the 2003 Mars exploration rovers (MER), require all rover commands to be determined on the ground, which is a time-consuming and largely manual process. However, many science opportunities can be efficiently handled by performing intelligent decision-making onboard the rover itself. This paper describes how dynamic planning and scheduling techniques can be used onboard a rover to autonomously adjust rover activities in support of science goals. These goals could be identified by scientists on the ground or could be identified by onboard data-analysis software. Several different types of dynamic decisions are described, including the handling of opportunistic science goals identified during rover traverses, preserving high priority science targets when resources, such as power, are unexpectedly oversubscribed, and dynamically adding additional, ground-specified science targets when rover actions are executed more quickly than expected. After describing our system approach, we discuss some of the particular challenges we have examined to support autonomous rover decision-making. These include interaction with rover navigation and path-planning software and handling large amounts of uncertainty in state and resource estimations. Finally, we describe our experiences in testing this work using several Mars rover prototypes in a realistic environment.