Feature Space Exploration For Planning Initial Benthic AUV Surveys
Feature Space Exploration For Planning Initial Benthic AUV Surveys
复制标题
用于规划初始海底 AUV 调查的特征空间探索
DOI：
10.55417/fr.2023021
复制
发表时间：
2021
期刊：
影响因子：
--
通讯作者：
Stefan B. Williams
中科院分区：
文献类型：
--
作者：
J. Shields;O. Pizarro;Stefan B. Williams
专用自主水下航行器 (AUV) 用于海底（海底）调查，收集海底的光学图像。由于相机的传感器占用空间小且要测量的区域广阔，这些 AUV 无法收集大于几万平方米的区域的全覆盖图像。因此，AUV 路径有必要对调查区域进行稀疏但有效的采样。大范围的声学测深数据在大范围内很容易获得，并且通常是海底覆盖的有用先验。因此，先前的测深可用于指导 AUV 数据收集。本研究提出了规划初始 AUV 调查的方法，该方法可以有效地探索测深的特征空间表示，以便从不同的测深地形中进行采样。这将使 AUV 能够访问可能包含独特栖息地并代表整个调查地点的区域。我们提出了几种信息收集规划器，它们利用特征空间探索奖励来规划自由路径或优化调查模板的放置。这些方法规划 AUV 调查的适用性是根据特征空间的覆盖范围以及在初次潜水时访问所有类别的底栖栖息地的能力来评估的。基于快速扩展随机树 (RRT) 和蒙特卡罗树搜索 (MCTS) 的信息规划器被发现是最有效的。这是 AUV 调查的一个有价值的工具，因为它提高了初始潜水的实用性。它还提供了一套全面的培训集，用于学习声学测深和视觉衍生的海底分类之间的关系。
Special-purpose Autonomous Underwater Vehicles (AUVs) are utilized for benthic (seafloor) surveys, where the vehicle collects optical imagery of the seafloor. Due to the small-sensor footprint of the cameras and the vast areas to be surveyed, these AUVs can not feasibly collect full coverage imagery of areas larger than a few tens of thousands of square meters. Therefore it is necessary for AUV paths to sample the surveys areas sparsely, yet effectively. Broad-scale acoustic bathymetric data are readily available over large areas and are often a useful prior of seafloor cover. As such, prior bathymetry can be used to guide AUV data collection. This research proposes methods for planning initial AUV surveys that efficiently explore a feature space representation of the bathymetry, in order to sample from a diverse set of bathymetric terrain. This will enable the AUV to visit areas that likely contain unique habitats and are representative of the entire survey site. We propose several information gathering planners that utilize a feature space exploration reward, to plan freeform paths or to optimize the placement of a survey template. The suitability of these methods to plan AUV surveys is evaluated based on the coverage of the feature space and also the ability to visit all classes of benthic habitat on the initial dive. Informative planners based on Rapidly expanding Random Trees (RRT) and Monte Carlo Tree Search (MCTS) were found to be the most effective. This is a valuable tool for AUV surveys as it increases the utility of initial dives. It also delivers a comprehensive training set to learn the relationship between acoustic bathymetry and visually derived seafloor classifications.