Adaptive Multi-Altitude Search and Sampling of Sparsely Distributed Natural Phenomena

In this paper, we propose a novel method for autonomously seeking out sparsely distributed targets in an un- known underwater environment. Our Sparse Adaptive Search and Sample (SASS) algorithm mixes low-altitude observations of discrete targets with high-altitude observations of the sur- rounding substrates. By using prior information about the dis- tribution of targets across substrate types in combination with belief modelling over these substrates in the environment, high- altitude observations provide information that allows SASS to quickly guide the robot to areas with high target densities. A maximally informative path is autonomously constructed online using Monte Carlo Tree Search with a novel acquisition function to guide the search to maximise observations of unique targets. We demonstrate our approach in a set of simulated trials using a novel generative species model. SASS consistently outperforms the canonical boustrophedon planner by up to 36% in seeking out unique targets in the first 75 - 90% of time it takes for a boustrophedon survey. Additionally, we verify the performance of SASS on two real world coral reef datasets.