用高斯过程指导探测路径，高效精准绘制未知星球表面分布
Informative Path Planning to Explore and Map Unknown Planetary Surfaces with Gaussian Processes
- 基于高斯过程的智能路径规划，动态更新地图信心度
- 误差降低40%以上，探索距离减少35%，收敛更快
- 适合深空探测、无人测绘等需精准建模的场景
许多环境，如未探测的行星表面和海洋区域，因缺乏先验知识而难以探索。自主车辆必须在抵达后采样、处理数据，并决定下一步行动或向远程操作员传输信息。遥操作效率低下，因人类直觉无法提供最优性保障。本研究评估了一种信息驱动的路径规划算法，在最小化行驶距离的同时，对标量变量分布进行地图构建并确保模型收敛。将传统开环覆盖方法（如牛耕式、螺旋式）与基于高斯过程的信息理论方法对比，后者通过置信度指标迭代更新模型。在抛物面、Townsend函数及月球陨石坑水合物分布图三类表面上测试，评估噪声、凸性及函数特性影响。结果表明，信息驱动方法显著优于盲目探索，在降低模型误差、减少旅行距离方面表现更优，且具备更强的收敛潜力。
原文摘要 · Abstract (English)
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.
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