Multiobjective Waypoint Sequencing for Planetary Rovers with Time-Dependent Energy Constraints

Abstract

Robots will be the first to discover and characterize ices that exist at the poles of some moons and planets. These distinctive regions have extensive, grazing, time-varying shadows that raise significant time and energy constraints for solarpowered robots. In order to maximize the sciencevalue of missions in such environments, rovers must visit as many targets as possible while considering limitations imposed by time-varying shadows and risks associated with traveling long distances. This paper compares a greedy baseline algorithm with two genetic algorithm approaches for selecting and sequencing waypoints to maximize waypoint value while minimizing distance traveled. The value and diversity of solutions from the baseline greedy solution, a single-objective genetic algorithm, and an NSGA-II framework are compared for this multiobjective optimization problem. All genetic solutions are shown to find high value sequences as compared to the greedy algorithm. This research demonstrates that a genetic approach could be utilized to effectively plan future missions for solar-powered rovers in dynamic, shadowed environments.