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The safe operation of drone swarms beyond visual line of sight requiresmultiple safeguards to mitigate the risk of collision between drones flying inhyper localised scenarios. Cooperative navigation and flight coordinationstrategies that rely on pre-planned trajectories and require constant networkconnectivity are brittle to failure. Drone embedded sense and detect offers acomprehensive mode of separation between drones for deconfliction and collisionavoidance. This paper presents the first airborne LiDAR based solution fordrone-swarm detection and localisation using 3D deep learning. It adapts andembeds the PointPillars deep learning neural network on the drone. To collecttraining data of close-quarter multi drone operations and safety criticalscenarios, a scenario Digital Twin is used to augment real datasets with highfidelity synthetic data. The method has been validated in real-world tests. Thetrained model achieves over 80% recall and 96% precision when tested on realdatasets. By incorporating a detection-by-tracking algorithm the system canreliably monitor the separation distance of multiple drones in challengingenvironments.

Airborne Sense and Detect of Drones using LiDAR and adapted PointPillars DNN