Autonomous and mobile cyber-physical machines are becoming an inevitable part of our future. In particular, Micro Aerial Vehicles (MAVs) have seen a resurgence in activity. With multiple use cases, such as surveillance, search and rescue, package delivery, and more, these unmanned aerial systems are on the cusp of demonstrating their full potential. Despite such promises, these systems face many challenges, one of the most prominent of which is their low endurance caused by their limited onboard energy. Since the success of a mission depends on whether the drone can finish it within such duration and before it runs out of battery, improving both the time and energy associated with the mission are of high importance. Such improvements have traditionally been arrived at through the use of better algorithms. But our premise is that more powerful and efficient onboard compute can also address the problem. In this article, we investigate how the compute subsystem, in a cyber-physical mobile machine such as a Micro Aerial Vehicle, can impact mission time (time to complete a mission) and energy. Specifically, we pose the question as what is the role of computing for cyber-physical mobile robots? We show that compute and motion are tightly intertwined, and as such a close examination of cyber and physical processes and their impact on one another is necessary. We show different “impact paths” through which compute impacts mission metrics and examine them using a combination of analytical models, simulation, and micro and end-to-end benchmarking. To enable similar studies, we open sourced MAVBench, our tool-set, which consists of (1) a closed-loop real-time feedback simulator and (2) an end-to-end benchmark suite composed of state-of-the-art kernels. By combining MAVBench, analytical modeling, and an understanding of various compute impacts, we show up to 2X and 1.8X improvements for mission time and mission energy for two optimization case studies, respectively. Our investigations, as well as our optimizations, show that cyber-physical co-design, a methodology with which both the cyber and physical processes/quantities of the robot are developed with consideration of one another, similar to hardware-software co-design, is necessary for arriving at the design of the optimal robot.

自主和移动的网络物理机器正在成为我们未来不可避免的一部分。特别是，微型飞行器 (MAV) 的活动已经复苏。凭借多种用例，例如监视、搜索和救援、包裹递送等，这些无人机系统正处于展示其全部潜力的风口浪尖。尽管有这样的承诺，但这些系统仍面临许多挑战，其中最突出的挑战之一是由于其有限的机载能量而导致的低耐久性。由于任务的成功取决于无人机能否在这样的持续时间内完成任务，并且在电池耗尽之前完成，因此改善与任务相关的时间和能量非常重要。传统上，此类改进是通过使用更好的算法来实现的。但我们的前提是，更强大、更高效的板载计算也可以解决这个问题。在本文中，我们研究了微型飞行器等网络物理移动机器中的计算子系统如何影响任务时间（完成任务的时间）和能量。具体来说，我们提出的问题是计算对于信息物理移动机器人的作用是什么？我们表明，计算和运动是紧密交织在一起的，因此有必要对网络和物理过程及其对彼此的影响进行仔细检查。我们展示了计算影响任务指标的不同“影响路径”，并使用分析模型、模拟以及微观和端到端基准测试的组合来检查它们。为了进行类似的研究，我们开源了 MAVBench，我们的工具集，包括（1）一个闭环实时反馈模拟器和（2）一个由最先进的内核组成的端到端基准测试套件。通过结合 MAVBench、分析建模和对各种计算影响的理解，我们展示了两个优化案例研究的任务时间和任务能量分别提高了 2 倍和 1.8 倍。我们的调查以及我们的优化表明，网络物理协同设计，一种在开发机器人的网络和物理过程/数量时相互考虑的方法，类似于硬件-软件协同设计，是设计最优机器人所必需的。