An Autonomous Drone Swarm for Detecting and Tracking Anomalies among Dense Vegetation

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 average positional accuracy of 0.39 m with an average precision of 93.2% and an average recall of 95.9%. Here, adapted particle swarm optimization considers detection confidences and predicted target appearance. We show that sensor noise can effectively be included in the synthetic aperture image integration process, removing the need for a computationally costly optimization of high-dimensional parameter spaces. Finally, we present a complete hard- and software framework that supports low-latency transmission (approx. 80 ms round-trip time) and fast processing (approx. 600 ms per formation step) of extensive (70-120 Mbit/s) video and telemetry data, and swarm control for swarms of up to ten drones.