Planetary Terrain Datasets and Benchmarks for Rover Path Planning
Marvin Chanc\'an, Avijit Banerjee, George Nikolakopoulos

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…
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Taxonomy
TopicsRobotic Path Planning Algorithms · Spacecraft Dynamics and Control · Planetary Science and Exploration
