Disturbance-Aware Aerial Robotics for Ethical Wildlife Monitoring

2026-06-06

AI Summary

Existing wildlife monitoring approaches often disturb animal behavior and lack scalable, non-invasive, and adaptive solutions. This work proposes a perturbation-aware reinforcement learning framework that, for the first time, integrates a behavior-model-based disturbance-aware mechanism into the control policy of heterogeneous drone swarms, enabling generalizable, non-intrusive tracking across species, platforms, and behavioral patterns. By leveraging a realistic simulation environment built from statistical models of animal movement trajectories, a behavior-aware reward function, and deep reinforcement learning training, the learned policy significantly outperforms rule-based baselines across three ecologically diverse species and four natural behavioral models, while demonstrating strong generalization capabilities.

Abstract

Reliable wildlife monitoring is essential for ecology and conservation, yet many existing methods, such as tagging, capture, and close-range observation, can alter the very behaviors they aim to measure. Aerial robots offer a scalable alternative, which has shown promising performance in multiple studies. Nonetheless, existing approaches typically lack behavioral awareness, rely on fixed heuristics, or require real-world training data that are costly, impractical, and ethically difficult to obtain. As a result, there remains no general framework for adaptive drone-based monitoring that can both preserve ecological validity and scale across species, behaviors, and robotic platforms. In this study, we introduce a disturbance-aware reinforcement-learning-based framework for heterogeneous aerial robotic fleets that enables autonomous wildlife tracking while explicitly minimizing behavioral disruption. We couple a zoologically grounded simulation environment with fitted animal movement models derived from real trajectory statistics, and train control policies using a reward formulation that captures the trade-off between observation quality and disturbance risk. Across three species (pigeon, jackal, and spur-winged lapwing) with distinct ecologies and motion patterns and four increasingly strategic behavior models common in nature, the learned policies consistently surpassed currently used rule-based baselines and generalized across monitoring tasks, animal dynamics, and drone types. These results establish disturbance-aware learning as a viable foundation for non-invasive autonomous wildlife observation, opening a path towards scalable, ethically responsible, and scientifically reliable robotic monitoring in ecology and conservation.

Problem

• wildlife monitoring
• behavioral disturbance
• aerial robotics
• ecological validity
• autonomous observation

Innovation

• disturbance-aware
• reinforcement learning
• aerial robotics
• wildlife monitoring
• behavioral disruption