神经形态姿态估计与控制
Neuromorphic Attitude Estimation and Control
AI总结：
针对小型无人机能源受限问题，提出基于模块化脉冲神经网络的神经形态姿态估计与控制方法，经数据增强训练后部署在Crazyflie上，姿态跟踪误差接近常规飞行栈，验证了端到端神经形态控制的可行性。
摘要：
小型无人机的实际应用主要受限于能源问题。神经形态计算有望为自主飞行提供极高能效的AI，但在真实机器人上的训练和部署仍具挑战性。为最大化发挥神经形态计算的优势，有必要在单颗神经形态芯片上端到端执行所有自主功能，涵盖从底层姿态控制到高层导航的全部环节。本研究提出了首个采用脉冲神经网络（SNN）的神经形态控制系统，可将无人机的原始传感器输入直接有效映射为电机指令。我们将该方法应用于四旋翼无人机的底层姿态估计与控制，并将SNN部署在微型的Crazyflie无人机上。我们提出了一种模块化SNN架构，分别训练估计子网络与控制子网络，随后将二者合并。该SNN采用模仿学习方法，基于包含感知-运动配对数据的飞行数据集进行训练。训练完成后，网络部署在Crazyflie上，可根据传感器输入以500Hz的频率输出控制指令。此外，在训练流程中，我们通过让控制器在附加激励下飞行并对目标数据进行时移处理来增强训练数据，以提升SNN的预测能力。在真实无人机上，该感知到控制的SNN跟踪姿态指令的平均误差为3.0度，而常规飞行栈的平均误差为2.7度。我们还验证了所提出的训练改进措施在降低平均跟踪误差、减少振荡方面的效果。本研究证明了神经形态端到端控制的可行性，为高能效、低延迟的神经形态自动驾驶仪奠定了基础。
英文摘要：
The real-world application of small drones is mostly hampered by energy limitations. Neuromorphic computing promises extremely energy-efficient AI for autonomous flight but is still challenging to train and deploy on real robots. To reap the maximal benefits from neuromorphic computing, it is necessary to perform all autonomy functions end-to-end on a single neuromorphic chip, from low-level attitude control to high-level navigation. This research presents the first neuromorphic control system using a spiking neural network (SNN) to effectively map a drone's raw sensory input directly to motor commands. We apply this method to low-level attitude estimation and control for a quadrotor, deploying the SNN on a tiny Crazyflie. We propose a modular SNN, separately training and then merging estimation and control sub-networks. The SNN is trained with imitation learning, using a flight dataset of sensory-motor pairs. Post-training, the network is deployed on the Crazyflie, issuing control commands from sensor inputs at 500Hz. Furthermore, for the training procedure we augmented training data by flying a controller with additional excitation and time-shifting the target data to enhance the predictive capabilities of the SNN. On the real drone, the perception-to-control SNN tracks attitude commands with an average error of 3.0 degrees, compared to 2.7 degrees for the regular flight stack. We also show the benefits of the proposed learning modifications for reducing the average tracking error and reducing oscillations. Our work shows the feasibility of performing neuromorphic end-to-end control, laying the basis for highly energy-efficient and low-latency neuromorphic autopilots.
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