Informative Path Planning to Explore and Map Unknown Planetary Surfaces with Gaussian Processes
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arXiv
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| Autori principali: | , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866908276691566592 |
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| author | Akemoto, Ashten Zhu, Frances |
| author_facet | Akemoto, Ashten Zhu, Frances |
| contents | Many environments, such as unvisited planetary surfaces and oceanic regions, remain unexplored due to a lack of prior knowledge. Autonomous vehicles must sample upon arrival, process data, and either transmit findings to a teleoperator or decide where to explore next. Teleoperation is suboptimal, as human intuition lacks mathematical guarantees for optimality. This study evaluates an informative path planning algorithm for mapping a scalar variable distribution while minimizing travel distance and ensuring model convergence. We compare traditional open loop coverage methods (e.g., Boustrophedon, Spiral) with information-theoretic approaches using Gaussian processes, which update models iteratively with confidence metrics. The algorithm's performance is tested on three surfaces, a parabola, Townsend function, and lunar crater hydration map, to assess noise, convexity, and function behavior. Results demonstrate that information-driven methods significantly outperform naive exploration in reducing model error and travel distance while improving convergence potential. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_16613 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Informative Path Planning to Explore and Map Unknown Planetary Surfaces with Gaussian Processes Akemoto, Ashten Zhu, Frances Robotics Information Retrieval Machine Learning Many environments, such as unvisited planetary surfaces and oceanic regions, remain unexplored due to a lack of prior knowledge. Autonomous vehicles must sample upon arrival, process data, and either transmit findings to a teleoperator or decide where to explore next. Teleoperation is suboptimal, as human intuition lacks mathematical guarantees for optimality. This study evaluates an informative path planning algorithm for mapping a scalar variable distribution while minimizing travel distance and ensuring model convergence. We compare traditional open loop coverage methods (e.g., Boustrophedon, Spiral) with information-theoretic approaches using Gaussian processes, which update models iteratively with confidence metrics. The algorithm's performance is tested on three surfaces, a parabola, Townsend function, and lunar crater hydration map, to assess noise, convexity, and function behavior. Results demonstrate that information-driven methods significantly outperform naive exploration in reducing model error and travel distance while improving convergence potential. |
| title | Informative Path Planning to Explore and Map Unknown Planetary Surfaces with Gaussian Processes |
| topic | Robotics Information Retrieval Machine Learning |
| url | https://arxiv.org/abs/2503.16613 |