An Autonomous Drone Swarm for Detecting and Tracking Anomalies among Dense Vegetation
Rakesh John Amala Arokia Nathan, Sigrid Strand, Daniel Mehrwald,, Dmitriy Shutin, Oliver Bimber

TL;DR
This paper presents an autonomous drone swarm system that detects and tracks anomalies in dense vegetation using synthetic aperture imaging and adaptive sampling, achieving high accuracy and robustness in real-world experiments.
Contribution
It introduces a novel anomaly detection method with synthetic aperture images and adaptive swarm control, improving robustness and efficiency over traditional classification approaches.
Findings
Achieved 0.39 m positional accuracy in field tests
Attained 93.2% precision and 95.9% recall in anomaly detection
Demonstrated real-time processing and control of up to ten drones
Abstract
Swarms of drones offer an increased sensing aperture, and having them mimic behaviors of natural swarms enhances sampling by adapting the aperture to local conditions. We demonstrate that such an approach makes detecting and tracking heavily occluded targets practically feasible. While object classification applied to conventional aerial images generalizes poorly the randomness of occlusion and is therefore inefficient even under lightly occluded conditions, anomaly detection applied to synthetic aperture integral images is robust for dense vegetation, such as forests, and is independent of pre-trained classes. Our autonomous swarm searches the environment for occurrences of the unknown or unexpected, tracking them while continuously adapting its sampling pattern to optimize for local viewing conditions. In our real-life field experiments with a swarm of six drones, we achieved an…
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Taxonomy
TopicsSpecies Distribution and Climate Change · Remote Sensing in Agriculture
