用脉冲神经网络实现微型飞行器在低成本芯片上的自主飞行控制。
Neuromorphic Control of a Flapping-Wing Robot on Resource-Constrained Hardware
- 分层设计两个轻量脉冲神经网络，分别负责状态估计和飞行控制。
- 相比传统神经网络，延迟降低36%、功耗减少18%，实测有效。
- 首次在5美元芯片上实现全自主飞行，适合资源受限场景。
扑翼微型飞行器（FWMAV）具有优异的机动性和气动效率，但受非线性动力学及尺寸、重量与功耗（SWaP）严格限制，如一款小于30克的蝴蝶仿生机器人。本文提出一种分层脉冲神经网络控制框架，可在广泛使用的资源受限ESP32微控制器（单价约5美元）上实现完全机载闭环飞行。系统部署两个轻量级脉冲神经网络（SNN）：一个用于从原始传感器反馈中进行状态估计，另一个通过调制中央模式生成器（CPG）实现翅膀驱动控制。通过模仿学习训练后，系统在无绳真实飞行中实现了稳定的俯仰角和航向角跟踪。实验表明，与传统人工神经网络（ANN）基线相比，SNN控制器推理时延降低36%（1059μs降至680μs），功耗减少18%（0.033W降至0.027W），证明了无需专用硬件即可实现基于脉冲计算的能效优势。据我们所知，这是首个在机载环境下实现全自主飞行的冯·诺依曼架构脉冲神经网络控制案例，凸显了SNN在严苛SWaP约束下实现节能自主性的潜力。
原文摘要 · Abstract (English)
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 Code: https://anonymous.4open.science/r/Espikify-76E3/
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