自动化优化神经网络，让微型无人机更智能省电。
Improving Autonomous Nano-drones Performance via Automated End-to-End Optimization and Deployment of DNNs
- 端到端自动优化视觉CNN模型，适配微型无人机算力
- 内存减半、推理提速1.6倍，精度不变且实测飞行更快
- 开源工具链，支持即插即用的微型无人机智能部署
超低功耗（ULP）并行处理器的发展与卷积神经网络（CNN）的普及，推动了自主纳米级无人飞行器（nano-UAV）的兴起。这些尺寸小于10厘米的机器人平台被视为下一代无处不在的智能传感器和隐蔽式机器人助手。然而，纳米无人机有限的计算与存储资源带来了视觉CNN模型的最小化与优化挑战——目前仍依赖繁琐的人工迭代开发流程。本文探索了方法与软件工具，实现基于ULP多核片上系统（SoC）的视觉导航CNN在Crazyflie 2.1纳米无人机上的全流程自动化部署。聚焦于前沿的PULP-Dronet模型，从训练到闭环测试全程自动化。相比原始手工设计的CNN，本工作实现内存占用减少50%（2倍）、推理速度提升1.6倍，同时保持相同预测精度，并显著提升实际飞行表现：实现最高1.65 m/s的紧急制动避障，速度/制动距离比优于基线；在熟悉环境中自由飞行最高速度达1.96 m/s（基线仅0.5 m/s）；完成带90°转弯的车道跟随任务。所有计算仅消耗无人机总功耗的1.6%以下。为促进新应用与未来研究，我们开源了完整的可运行项目，兼容Crazyflie 2.1平台。
原文摘要 · Abstract (English)
The evolution of energy-efficient ultra-low-power (ULP) parallel processors and the diffusion of convolutional neural networks (CNNs) are fueling the advent of autonomous driving nano-sized unmanned aerial vehicles (UAVs). These sub-10 cm robotic platforms are envisioned as next-generation ubiquitous smart-sensors and unobtrusive robotic-helpers. However, the limited computational/memory resources available aboard nano-UAVs introduce the challenge of minimizing and optimizing vision-based CNNs -- which to date require error-prone, labor-intensive iterative development flows. This work explores methodologies and software tools to streamline and automate all the deployment of vision-based CNN navigation on a ULP multicore system-on-chip acting as a mission computer on a Crazyflie 2.1 nano-UAV. We focus on the deployment of PULP-Dronet, a state-of-the-art CNN for autonomous navigation of nano-UAVs, from the initial training to the final closed-loop evaluation. Compared to the original hand-crafted CNN, our results show a 2x reduction of memory footprint and a speedup of 1.6x in inference time while guaranteeing the same prediction accuracy and significantly improving the behavior in the field, achieving: i) obstacle avoidance with a peak braking-speed of 1.65 m/s and improving the speed/braking-space ratio of the baseline, ii) free flight in a familiar environment up to 1.96 m/s (0.5 m/s for the baseline), and iii) lane following on a path featuring a 90 deg turn -- all while using for computation less than 1.6% of the drone's power budget. To foster new applications and future research, we open-source all the software design in a ready-to-run project compatible with the Crazyflie 2.1
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