Building domain-specific architectures for autonomous aerial robots is challenging due to a lack of systematic methodology for designing onboard compute. We introduce a novel performance model called the F-1 roofline to help architects understand how to build a balanced computing system for autonomous aerial robots considering both its cyber (sensor rate, compute performance) and physical components (body-dynamics) that affect the performance of the machine. We use F-1 to characterize commonly used learning-based autonomy algorithms with onboard platforms to demonstrate the need for cyber-physical co-design. To navigate the cyber-physical design space automatically, we subsequently introduce AutoPilot. This push-button framework automates the co-design of cyber-physical components for aerial robots from a high-level specification guided by the F-1 model. AutoPilot uses Bayesian optimization to automatically co-design the autonomy algorithm and hardware accelerator while considering various cyber-physical parameters to generate an optimal design under different task level complexities for different robots and sensor framerates. As a result, designs generated by AutoPilot, on average, lower mission time up to 2x over baseline approaches, conserving battery energy.

中文翻译：

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基于机器学习的自动航空机器人自动设计空间探索

由于缺乏用于设计机载计算的系统方法，为自动飞行机器人构建特定于领域的架构具有挑战性。我们引入了一种称为F-1车顶线的新颖性能模型，以帮助建筑师了解影响其性能的网络（传感器速率，计算性能）和物理组件（身体动力学）如何为自动飞行机器人构建平衡的计算系统。机器的 我们使用F-1来表征常用的基于学习的自主算法和机载平台，以证明需要进行网络物理协同设计。为了自动导航物理物理设计空间，我们随后引入了AutoPilot。这个按钮式框架使F-1模型指导的高级规范自动化了航空机器人网络物理组件的协同设计。AutoPilot使用贝叶斯优化来自动设计自治算法和硬件加速器，同时考虑各种网络物理参数，以针对不同的机器人和传感器帧速率在不同任务级别复杂性下生成最佳设计。结果，由AutoPilot生成的设计平均可将任务时间缩短至基线方法的2倍，从而节省了电池能量。