Planetary Terrain Datasets and Benchmarks for Rover Path Planning

TL;DR

This paper introduces new planetary terrain datasets and benchmarks for rover path planning, evaluates classical and learning algorithms, and provides insights into their performance on Mars and Moon terrains.

Contribution

It presents the first large planetary terrain benchmark datasets and a unified evaluation framework for path planning algorithms in space exploration.

Findings

Classical algorithms achieve up to 100% success rates on challenging terrains.

Learning-based models struggle to generalize to planetary environments.

The datasets and code will be publicly released for future research.

Abstract

Planetary rover exploration is attracting renewed interest with several upcoming space missions to the Moon and Mars. However, a substantial amount of data from prior missions remain underutilized for path planning and autonomous navigation research. As a result, there is a lack of space mission-based planetary datasets, standardized benchmarks, and evaluation protocols. In this paper, we take a step towards coordinating these three research directions in the context of planetary rover path planning. We propose the first two large planar benchmark datasets, MarsPlanBench and MoonPlanBench, derived from high-resolution digital terrain images of Mars and the Moon. In addition, we set up classical and learned path planning algorithms, in a unified framework, and evaluate them on our proposed datasets and on a popular planning benchmark. Through comprehensive experiments, we report new insights on the performance of representative path planning algorithms on planetary terrains, for the first time to the best of our knowledge. Our results show that classical algorithms can achieve up to 100% global path planning success rates on average across challenging terrains such as Moon's north and south poles. This suggests, for instance, why these algorithms are used in practice by NASA. Conversely, learning-based models, although showing promising results in less complex environments, still struggle to generalize to planetary domains. To serve as a starting point for fundamental path planning research, our code and datasets will be released at: https://github.com/mchancan/PlanetaryPathBench.