/Science-Aware Exploration Using Entropy-Based Planning

Science-Aware Exploration Using Entropy-Based Planning

Shivam Gautam, Bishwamoy Sinha Roy, Alberto Candela and David Wettergreen
Conference Paper, 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2017), September, 2017

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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 tradeoff 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.

BibTeX Reference
author = {Shivam Gautam and Bishwamoy Sinha Roy and Alberto Candela and David Wettergreen},
title = {Science-Aware Exploration Using Entropy-Based Planning},
booktitle = {2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2017)},
year = {2017},
month = {September},