Neuromorphic Control of a Flapping-Wing Robot on Resource-Constrained Hardware

TL;DR

This paper introduces a neuromorphic control system using spiking neural networks for a small, resource-limited flapping-wing drone, enabling stable onboard autonomous flight with reduced latency and power consumption.

Contribution

It presents the first fully onboard neuromorphic control framework for a flapping-wing micro aerial vehicle on a low-cost microcontroller.

Findings

Achieved stable pitch and heading control during real-world flight.

Reduced inference latency by 36% compared to traditional neural networks.

Lowered power consumption by 18% during inference.

Abstract

Flapping-Wing Micro Aerial Vehicles (FWMAVs) provide exceptional maneuverability and aerodynamic efficiency but pose significant challenges for onboard control due to nonlinear dynamics and stringent Size, Weight, and Power (SWaP) constraints, as exemplified by a butterfly-inspired robot less than 30 gram. To this end, we present a hierarchical neuromorphic control framework that enables fully onboard, closed-loop flight on a widely available, resource-constrained ESP32 microcontroller with a unit cost of approximately $5. Specifically, our method deploys two lightweight Spiking Neural Networks (SNNs) onboard: one for state estimation from raw sensory feedback and another for control via modulation of a Central Pattern Generator (CPG) for wing actuation. Trained by imitation learning, the system achieves stable pitch and heading angle tracking during untethered real-world flight. Experimental results further reveal that the SNN-based controller reduces latency by 36% (1059us to 680us) and power by 18% (0.033W to 0.027W) for inference compared to the conventional Artificial Neural Network (ANN) baseline, demonstrating the viability of spike-based computation without specialized hardware. To the best of our knowledge, this work constitutes the first demonstration of fully onboard neuromorphic control for autonomous flight of a FWMAV, highlighting the potential of SNNs to enable energy-efficient autonomy under stringent SWaP constraints. Visual abstract: http://bit.ly/4nI8ECY