This paper reports an investigation into the problem of rapid identification of a channel that crosses a body of water using one or more Unmanned Surface Vehicles (USV). A new algorithm called Proposal Based Adaptive Channel Search (PBACS) is presented as a potential solution that improves upon current methods. The empirical performance of PBACS is compared to lawnmower surveying and to Markov decision process (MDP) planning with two state-of-the-art reward functions: Upper Confidence Bound (UCB) and Maximum Value Information (MVI). The performance of each method is evaluated through comparison of the time it takes to identify a continuous channel through an area, using one, two, three, or four USVs. The performance of each method is compared across ten simulated bathymetry scenarios and one field area, each with different channel layouts. The results from simulations and field trials indicate that on average multi-vehicle PBACS outperforms lawnmower, UCB, and MVI based methods, especially when at least three vehicles are used.

翻译：本文研究了利用一台或多台无人水面航行器（USV）快速识别水域中通航通道的问题。提出了一种名为"基于提议的自适应通道搜索"（PBACS）的新算法，作为改进现有方法的潜在解决方案。将PBACS的经验性能与割草机式测量法以及采用两种最先进奖励函数（上置信界UCB和最大价值信息MVI）的马尔可夫决策过程（MDP）规划方法进行了对比。通过比较使用1、2、3或4台USV识别连续通道所需的时间，评估了每种方法的性能。在十种模拟水深地形场景和一个实际测试区域（各具不同通道布局）中对比了各方法的性能。仿真和现场试验结果表明，多航行器PBACS平均优于基于割草机式、UCB和MVI的方法，尤其在使用至少三台航行器时效果最为显著。