Towards optimizing swarm drone delivery in RF-denied environments

Swarm drone delivery has the potential to enhance reliability and scalability, but challenges in communication, coordination, redundancy, and handling complex scenarios still exist. This paper introduces an infrared-based Leader-Follower drone coordination system with load-sensing for low-light and RF-denied environments. The system employs a tree hierarchical network topology, where the leader drone transmits pose data via IR, and follower drones, equipped with NoIR cameras, process IR light patterns in real-time using convolutional neural networks and Fourier Transform. IR detection achieved 87% accuracy at 0.7 m in normal light and 95% at 1 m in low light. Additionally, this work presents string pose estimation and self-balancing tray models for balanced delivery. SPE uses symmetrical tethers to manage payload swing, real-time load adjustment, and even-load distribution, with a precision of 0.87, recall of 1.0, and F1 score of 0.93. SBT dynamically adjusts elastic tethers for minor imbalances. The system demonstrated considerable flight stability, maintaining minimal deviations in roll, pitch, and yaw, thus ensuring smooth and controlled drone movements. These innovations enhance drone stability and accuracy, making the system robust for challenging delivery environments.