Disturbance-Aware Aerial Robotics for Ethical Wildlife Monitoring

• URL: http://arxiv.org/abs/2606.08249v1
• Date: Sat, 06 Jun 2026 16:22:17 GMT
• Title: Disturbance-Aware Aerial Robotics for Ethical Wildlife Monitoring
• Abstract summary: We introduce a disturbance-aware reinforcement-learning-based framework for heterogeneous aerial robotic fleets.<n>We train control policies using a reward formulation that captures the trade-off between observation quality and disturbance risk.<n>These results establish disturbance-aware learning as a viable foundation for non-invasive autonomous wildlife observation.
• Score: 4.817429789586127
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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.

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