Neuromorphic Digital-Twin-based Controller for Indoor Multi-UAV Systems Deployment

TL;DR

This paper presents a distributed neuromorphic control framework for multi-UAV systems that combines cloud-edge computing, spike coding, and nature-inspired control to enable robust, energy-efficient formation and obstacle avoidance in complex environments.

Contribution

It introduces a novel neuromorphic, cloud-edge architecture with SNNs for autonomous multi-UAV control, reducing computational load and enhancing robustness during communication failures.

Findings

Achieved nearly 90% reduction in computational burden compared to traditional neural networks.

Demonstrated effective indoor deployment of 15 UAVs with formation control.

Validated collision-free obstacle avoidance for a 6-UAV flock.

Abstract

Presented study introduces a novel distributed cloud-edge framework for autonomous multi-UAV systems that combines the computational efficiency of neuromorphic computing with nature-inspired control strategies. The proposed architecture equips each UAV with an individual Spiking Neural Network (SNN) that learns to reproduce optimal control signals generated by a cloud-based controller, enabling robust operation even during communication interruptions. By integrating spike coding with nature-inspired control principles inspired by Tilapia fish territorial behavior, our system achieves sophisticated formation control and obstacle avoidance in complex urban environments. The distributed architecture leverages cloud computing for complex calculations while maintaining local autonomy through edge-based SNNs, significantly reducing energy consumption and computational overhead compared to traditional centralized approaches. Our framework addresses critical limitations of conventional methods, including the dependency on pre-modeled environments, computational intensity of traditional methods, and local minima issues in potential field approaches. Simulation results demonstrate the system's effectiveness across two different scenarios. First, the indoor deployment of a multi-UAV system made-up of 15 UAVs. Then the collision-free formation control of a moving UAV flock including 6 UAVs considering the obstacle avoidance. Owing to the sparsity of spiking patterns, and the event-based nature of SNNs in average for the whole group of UAVs, the framework achieves almost 90% reduction in computational burden compared to traditional von Neumann architectures implementing traditional artificial neural networks.