Automated Onboard Mission Planning for Robust and Flexible Rover Operations

Abstract

Activity planning for space mission operations has traditionally been a human-in-the-loop effort, conducted by ground operators. The outputs of the mission planning process are scripted sequences of activities that are uplinked to the space vehicle for execution. Over the past two decades, advances have been made toward automating the mission planning process, in an effort to improve the efficiency of the mission operations system, while increasing the mission return. Some aspects of onboard mission planning are increasingly used for complex missions, particularly for planetary surface missions that are subject to long communication delays. This paper applies an automated mission planning framework to a resource-constrained science rover mission case study. The plans are optimized on the basis of science return, accommodating traverse to sites of scientific interest according to ground-team preferences, while staying within rover engineering and traverse-related constraints. Automated mission planners offer the capability to schedule engineering and science activities onboard, without ground-in-the-loop interaction. Resource modeling and path planning can be done onboard, reducing the need for modeling and validation by ground operators. Further, automated mission planners may incorporate an optimization executive that maximizes the mission return within the available resource constraints. The proposed planners may be utilized on the ground by mission planning teams to provide additional insight during the planning process, or onboard autonomous rovers with limited human support. Using optimization methods, the developed automated mission planner establishes the planned sequence of routes to be followed to sites of high scientific value while adhering to constraints imposed by pathing requirements and resource availability. The activity plans coordinate the traverse planning and science data acquisition within the context of the evolving knowledge of the scientific value of the nearby terrain. The automated mission planning framework is designed to be adapted based upon the application. Optimization methods suitable for different mission planning problems are presented, comparing methods on the basis of computation speed, resources required and solution optimality. Measures of “robustness” and “flexibility” are incorporated into the framework to enable the system to adapt to changing conditions without violating constraints, and to provide additional criteria with which to evaluate and compare the produced activity plans.