Queensland University of Technology, Brisbane, QLD 4000, Australia.
CROSSING, CNRS, Adelaide, SA 5000, Australia.
Sensors (Basel). 2021 Feb 5;21(4):1115. doi: 10.3390/s21041115.
This paper addresses the challenge of embedded computing resources required by future autonomous Unmanned Aircraft Systems (UAS). Based on an analysis of the required onboard functions that will lead to higher levels of autonomy, we look at most common UAS tasks to first propose a classification of UAS tasks considering categories such as flight, navigation, safety, mission and executing entities such as human, offline machine, embedded system. We then analyse how a given combination of tasks can lead to higher levels of autonomy by defining an autonomy level. We link UAS applications, the tasks required by those applications, the autonomy level and the implications on computing resources to achieve that autonomy level. We provide insights on how to define a given autonomy level for a given application based on a number of tasks. Our study relies on the state-of-the-art hardware and software implementations of the most common tasks currently used by UAS, also expected tasks according to the nature of their future missions. We conclude that current computing architectures are unlikely to meet the autonomy requirements of future UAS. Our proposed approach is based on dynamically reconfigurable hardware that offers benefits in computational performance and energy usage. We believe that UAS designers must now consider the embedded system as a masterpiece of the system.
本文针对未来自主无人机系统 (UAS) 所需的嵌入式计算资源提出了挑战。通过对未来更高自主性所需的板载功能的分析，我们着眼于最常见的 UAS 任务，首先根据飞行、导航、安全、任务等类别，以及人类、离线机器、嵌入式系统等执行实体，对 UAS 任务进行分类。然后，我们通过定义自主性级别来分析给定任务组合如何导致更高的自主性。我们将无人机应用程序、这些应用程序所需的任务、自主性级别以及实现该自主性级别的计算资源联系起来。我们提供了一些见解，介绍了如何根据多项任务为给定应用程序定义给定的自主性级别。我们的研究依赖于当前最常见的 UAS 任务的硬件和软件的最新实现，以及根据其未来任务的性质预计的任务。我们得出的结论是，当前的计算架构不太可能满足未来 UAS 的自主性要求。我们提出的方法基于动态可重构硬件，它在计算性能和能源使用方面具有优势。我们认为，UAS 设计人员现在必须将嵌入式系统视为系统的杰作。
