Poster: An Edge-Native Approach to Behavior-Adaptive Navigation in Drone Systems

Le résumé fourni par la source

Unmanned aerial vehicles, i.e., drones, are well-suited to monitor wildlife behaviors in remote habitats as they can quickly traverse rough terrain inaccessible to humans. However, drone operators must balance (1) potentially aggressive flight tactics to adequately monitor species of interest against (2) animal welfare and the stress that the drone may induce. The navigation strategy must adapt during flight in real-time based on the animals' reactions, while abiding by resource constraints imposed by the environment. We propose an edge-native framework that infers wildlife behaviors during flight and adapts navigation to consider animal welfare. When behavior-adaptive flight (BAF) miscues, the framework engages human-on-the-loop (HoTL) supervision, a light-weight solution to integrate expert feedback. We have validated our approach with high-fidelity digital twin simulations and real-world deployments. Our edge-native system integrates YOLO-v11m object detection and YOLO-Behavior action recognition, operating at 23.8ms inference latency while maintaining strict animal welfare thresholds.

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