Bounomodes: the grazing ox algorithm for exploration of clustered anomalies

TL;DR

This paper introduces Bounomodes, an algorithm that combines uniform area coverage with targeted exploration of clustered anomalies using deep reinforcement learning, improving anomaly detection efficiency.

Contribution

The paper presents a novel bounomodes algorithm that alternates between uniform sampling and learned targeted exploration of anomaly clusters, enhancing anomaly detection in spatial environments.

Findings

Outperforms baseline methods in anomaly detection tasks.

Effectively learns to explore anomaly clusters using deep reinforcement learning.

Balances uniform coverage with targeted exploration for improved efficiency.

Abstract

A common class of algorithms for informative path planning (IPP) follows boustrophedon ("as the ox turns") patterns, which aim to achieve uniform area coverage. However, IPP is often applied in scenarios where anomalies, such as plant diseases, pollution, or hurricane damage, appear in clusters. In such cases, prioritizing the exploration of anomalous regions over uniform coverage is beneficial. This work introduces a class of algorithms referred to as bounom\=odes ("as the ox grazes"), which alternates between uniform boustrophedon sampling and targeted exploration of detected anomaly clusters. While uniform sampling can be designed using geometric principles, close exploration of clusters depends on the spatial distribution of anomalies and must be learned. In our implementation, the close exploration behavior is learned using deep reinforcement learning algorithms. Experimental evaluations demonstrate that the proposed approach outperforms several established baselines.