Abstract This study addresses subsea search applications where an autonomous underwater vehicle (AUV) is tasked with finding the target density in a given search region within finite time. We assume that AUV is equipped with a side-scan sonar sensor that detects the targets at the sampled location. We consider that sensor performance is dependent on local environmental conditions (e.g., clutter density, sediment type) that vary throughout the search region, and we presume that environmental conditions are unknown or partially known. Due to uncertain and varying environmental conditions, resulting search performance is also uncertain and it varies by location. This paper specifically considers the cases where environmental information can be acquired either by a separate vehicle or by the same vehicle that performs the search task. Our main contribution is to formally derive a decision-theoretic cost function to compute the locations where the environmental information should be acquired so that the performance of the search task can be improved. For the cases where computing the optimal locations to sample the environment is computationally expensive, we offer an approximation approach that yields provable near-optimal paths. We show that our decision-theoretic cost function outperforms the information-maximization approach, which is often employed in similar applications.

中文翻译：

---

一种获取环境信息以提高海底搜索性能的决策理论方法

摘要 这项研究解决了海底搜索应用，其中自主水下航行器 (AUV) 的任务是在有限时间内找到给定搜索区域中的目标密度。我们假设 AUV 配备了侧扫声纳传感器，可以检测采样位置的目标。我们认为传感器性能取决于在整个搜索区域内变化的局部环境条件（例如，杂波密度、沉积物类型），并且我们假设环境条件未知或部分已知。由于环境条件的不确定性和变化，导致搜索性能也不确定并且因位置而异。本文特别考虑了环境信息可以由单独的车辆或执行搜索任务的同一辆车获取的情况。我们的主要贡献是正式推导出决策理论成本函数来计算应获取环境信息的位置，从而提高搜索任务的性能。对于计算环境采样的最佳位置的计算成本很高的情况，我们提供了一种近似方法，可产生可证明的近乎最佳路径。我们表明，我们的决策理论成本函数优于通常用于类似应用的信息最大化方法。对于计算环境采样的最佳位置的计算成本很高的情况，我们提供了一种近似方法，可以产生可证明的近乎最佳路径。我们表明，我们的决策理论成本函数优于通常用于类似应用的信息最大化方法。对于计算环境采样的最佳位置的计算成本很高的情况，我们提供了一种近似方法，可以产生可证明的近乎最佳路径。我们表明，我们的决策理论成本函数优于通常用于类似应用的信息最大化方法。