Evolved neuromorphic radar-based altitude controller for an autonomous open-source blimp

TL;DR

This paper introduces an evolved spiking neural network-based altitude controller for a lightweight, open-source robotic airship, leveraging radar feedback and evolutionary training to achieve accurate altitude tracking with low power consumption.

Contribution

It presents a novel neuromorphic control architecture for airships, combining SNNs, evolutionary training, and real-world validation, addressing weight and power constraints.

Findings

Accurate altitude tracking demonstrated in real-world tests.

SNN controller outperforms traditional neural networks and linear controllers.

Efficient control effort achieved with neuromorphic approach.

Abstract

Robotic airships offer significant advantages in terms of safety, mobility, and extended flight times. However, their highly restrictive weight constraints pose a major challenge regarding the available computational resources to perform the required control tasks. Neuromorphic computing stands for a promising research direction for addressing such problem. By mimicking the biological process for transferring information between neurons using spikes or impulses, spiking neural networks (SNNs) allow for low power consumption and asynchronous event-driven processing. In this paper, we propose an evolved altitude controller based on an SNN for a robotic airship which relies solely on the sensory feedback provided by an airborne radar. Starting from the design of a lightweight, low-cost, open-source airship, we also present an SNN-based controller architecture, an evolutionary framework for training the network in a simulated environment, and a control strategy for ameliorating the gap with reality. The system's performance is evaluated through real-world experiments, demonstrating the advantages of our approach by comparing it with an artificial neural network and a linear controller. The results show an accurate tracking of the altitude command with an efficient control effort.