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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2508.15038 |
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| _version_ | 1866918128236101632 |
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| author | Chahine, Makram Yang, William Maalouf, Alaa Siriska, Justin Jadhav, Ninad Vogt, Daniel Gil, Stephanie Wood, Robert Rus, Daniela |
| author_facet | Chahine, Makram Yang, William Maalouf, Alaa Siriska, Justin Jadhav, Ninad Vogt, Daniel Gil, Stephanie Wood, Robert Rus, Daniela |
| contents | Wildlife field operations demand efficient parallel deployment methods to identify and interact with specific individuals, enabling simultaneous collective behavioral analysis, and health and safety interventions. Previous robotics solutions approach the problem from the herd perspective, or are manually operated and limited in scale. We propose a decentralized vision-based multi-quadrotor system for wildlife monitoring that is scalable, low-bandwidth, and sensor-minimal (single onboard RGB camera). Our approach enables robust identification and tracking of large species in their natural habitat. We develop novel vision-based coordination and tracking algorithms designed for dynamic, unstructured environments without reliance on centralized communication or control. We validate our system through real-world experiments, demonstrating reliable deployment in diverse field conditions. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_15038 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Decentralized Vision-Based Autonomous Aerial Wildlife Monitoring Chahine, Makram Yang, William Maalouf, Alaa Siriska, Justin Jadhav, Ninad Vogt, Daniel Gil, Stephanie Wood, Robert Rus, Daniela Robotics Artificial Intelligence Computer Vision and Pattern Recognition Multiagent Systems I.2.9 Wildlife field operations demand efficient parallel deployment methods to identify and interact with specific individuals, enabling simultaneous collective behavioral analysis, and health and safety interventions. Previous robotics solutions approach the problem from the herd perspective, or are manually operated and limited in scale. We propose a decentralized vision-based multi-quadrotor system for wildlife monitoring that is scalable, low-bandwidth, and sensor-minimal (single onboard RGB camera). Our approach enables robust identification and tracking of large species in their natural habitat. We develop novel vision-based coordination and tracking algorithms designed for dynamic, unstructured environments without reliance on centralized communication or control. We validate our system through real-world experiments, demonstrating reliable deployment in diverse field conditions. |
| title | Decentralized Vision-Based Autonomous Aerial Wildlife Monitoring |
| topic | Robotics Artificial Intelligence Computer Vision and Pattern Recognition Multiagent Systems I.2.9 |
| url | https://arxiv.org/abs/2508.15038 |