用高斯过程动态规划路径，提升海洋藻华监测精度。
Multi-Step Gaussian Process Propagation for Adaptive Path Planning
- 基于高斯过程建模环境不确定性，动态优化未来路径点。
- 相同采样数下误分类率降低，对藻华区域识别更准确。
- 适合需要实时适应多源传感数据的自主航行器任务。
高效且稳健的路径规划依赖于整合所有可获取的信息源。在机器人环境探索与监测任务中，路径规划高度依赖对当前世界状态的信念。为捕捉信念中的不确定性，本文提出一种基于高斯过程的自适应路径规划方法，可处理多模态环境传感数据，并融入状态与输入约束。通过滚动时域优化未来路径点，目标函数为所有路径点上高斯过程后验的函数。所提方法（OLA hGP）在自主水面舰艇上进行验证，使用高保真模型和实地传感数据进行藻华监测。仿真与实验结果表明，相比现有方法有显著提升：在相同采样数量下，生成的路径更具信息量，对叶绿素a含量高的水域中藻华识别的总误分类概率和域内二元误分类率均更低。
原文摘要 · Abstract (English)
Efficient and robust path planning hinges on combining all accessible information sources. In particular, the task of path planning for robotic environmental exploration and monitoring depends highly on the current belief of the world. To capture the uncertainty in the belief, we present a Gaussian process based path planning method that adapts to multi-modal environmental sensing data and incorporates state and input constraints. To solve the path planning problem, we optimize over future waypoints in a receding horizon fashion, and our cost is thus a function of the Gaussian process posterior over all these waypoints. We demonstrate this method, dubbed OLAhGP, on an autonomous surface vessel using oceanic algal bloom data from both a high-fidelity model and in-situ sensing data in a monitoring scenario. Our simulated and experimental results demonstrate significant improvement over existing methods. With the same number of samples, our method generates more informative paths and achieves greater accuracy in identifying algal blooms in chlorophyll a rich waters, measured with respect to total misclassification probability and binary misclassification rate over the domain of interest.
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