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.
翻译:建立自动航空机器人的域别架构具有挑战性,原因是缺乏系统化的在船上进行计算的设计方法。我们引入了名为 F-1 的新型性能模型,以帮助建筑师了解如何在考虑影响机器性能的网络机器人(传感器率、计算性能)和物理部件(机体动力学)的同时,为自动航空机器人建立一个平衡的计算系统。我们使用F-1来描述在机上平台上常用的基于学习的自主算法的特点,以表明对网络物理共同设计的需求。为了自动浏览网络物理设计空间,我们随后引入了AutoPilot。这个推键框架将AutPilot从F-1模型指导的高级规格中自动设计用于航空机器人的网络物理组件。AutoPilot使用Bayesian优化自动共同设计自主算法和硬件加速器,同时考虑各种网络物理参数,以便在不同任务级别复杂的情况下为不同的机器人和传感器框架设计最佳设计。结果是AutoPilot在平均、较低任务时间到2x基线方法上,由AutalPilot产生的设计。