Science-aware exploration using entropy-based planning

Abstract

Efficient exploration of unknown terrains by extraterrestrial rovers requires the development of strategies that reduce the entropy in the geological classification of a given terrain. Without such intelligent strategies, teleoperation of the rover is reliant either on human intuition or on the exhaustive exploration of the entire terrain. This paper highlights the use of low-resolution reconnaissance using satellite imagery to generate plans for rovers that reduce the overall uncertainty in the various geological classes. This becomes pivotal when exploration to collect diverse samples is resource constrained through exploration budgets and transmission bandwidths. We put forward two major contributions - a science-aware planner that uses information gain and a novel method of estimating this information gain. We propose an exploration strategy, based on the Multi-Heuristic A*, to solve the trade-off between optimizing path lengths and geological exploration through Pareto-optimal solutions. We show that our algorithm, which explicitly uses projected entropy-reduction in planning, significantly outperforms science-agnostic approaches and other science-aware strategies like greedy best-first searches. We further propose a feature-space based entropy formulation in contrast to the frequently used differential entropy formulation and show superior results when reconstructing the unsampled data from the set of sampled points.