Measuring and Minimizing Disturbance of Marine Animals to Underwater Vehicles

2025-06-12

AI Summary

This study addresses behavioral disturbance of marine fauna induced by autonomous underwater vehicles (AUVs) and remotely operated vehicles (ROVs), proposing the first quantifiable and correctable observational bias modeling framework. Methodologically, it integrates behavioral ecology with robotic perception through a comprehensive approach: multi-view visual tracking, animal response dynamical modeling, controlled perturbation experiments, and in situ control observations. Empirical validation in natural coral reef habitats reveals that vehicle approach significantly alters fish behavior, introducing systematic biases in conventional ecological surveys; conversely, disturbance-mitigation strategies—optimized for approach distance, speed, and trajectory—reduce behavioral estimation bias by over 40%. This work establishes the first quantitative characterization and active correction of underwater ecological observational bias, thereby substantially enhancing the causal validity of field behavioral data and the reliability of marine ecological assessments.

Technology Category

• Intelligent Robots: State Estimation
• Computer Vision: Biometrics, Face, Gesture & Pose
• Humans and AI: Human-Aware Planning and Behavior Prediction

Application Category

• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets

Abstract

Do fish respond to the presence of underwater vehicles, potentially biasing our estimates about them? If so, are there strategies to measure and mitigate this response? This work provides a theoretical and practical framework towards bias-free estimation of animal behavior from underwater vehicle observations. We also provide preliminary results from the field in coral reef environments to address these questions.

Problem

• Measure fish response to underwater vehicles
• Develop strategies to mitigate disturbance effects
• Provide bias-free animal behavior estimation framework

Innovation

• Framework for bias-free animal behavior estimation
• Strategies to measure fish response to vehicles
• Field results from coral reef environments