Bounomodes: the grazing ox algorithm for exploration of clustered anomalies

2025-07-09

AI Summary

To address the challenge in information path planning (IPP) for clustered anomalies—such as plant diseases, environmental contamination, or hurricane damage—where existing methods struggle to balance global coverage with local anomaly-focused exploration, this paper proposes an alternating hybrid path planning framework. The method integrates Boustrophedon-based global scanning with a deep reinforcement learning (DRL)-driven local adaptive exploration module. The DRL agent explicitly models the spatial distribution of anomaly clusters and dynamically allocates sensing resources, while geometric constraints guarantee a theoretical lower bound on coverage. Anomaly response prioritization is achieved through DRL-based scheduling. Experiments demonstrate that the proposed approach significantly outperforms multiple state-of-the-art IPP baselines across three key metrics: anomaly detection rate, localization accuracy, and path efficiency—particularly under low signal-to-noise ratio and sparse prior knowledge conditions.

Technology Category

• Intelligent Robots: Motion and Path Planning
• Planning, Routing, and Scheduling: Replanning and Plan Repair
• Search and Optimization: Mixed Discrete/Continuous Search

Application Category

• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs
• Search and Retrieval-Augmented AI: Agentic search

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 bounomōdes ("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.

Problem

• Optimizing path planning for clustered anomaly exploration
• Balancing uniform coverage with targeted anomaly detection
• Learning cluster exploration strategies via reinforcement learning

Innovation

• Alternates uniform and targeted anomaly exploration
• Uses deep reinforcement learning for cluster exploration
• Combines boustrophedon sampling with adaptive clustering