Towards computational awareness in autonomous robots: an empirical study of computational kernels
Towards computational awareness in autonomous robots: an empirical study of computational kernels
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DOI：
10.1007/s40747-023-01059-7
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发表时间：
2021-12
影响因子：
5.8
通讯作者：
Ashrarul H. Sifat;Burhanuddin Bharmal;Haibo Zeng;Jiabin Huang;Changhee Jung;Ryan K. Williams
中科院分区：
文献类型：
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作者：
Ashrarul H. Sifat;Burhanuddin Bharmal;Haibo Zeng;Jiabin Huang;Changhee Jung;Ryan K. Williams
自主机器人对日常生活的潜在影响在精准农业、搜索救援和基础设施检查等新兴应用中显而易见。然而，此类应用程序需要在未知和非结构化环境中运行，并具有广泛而复杂的目标，所有这些都受到严格的计算和功率限制。因此，我们认为，必须对实现机器人自治的计算内核进行调度和优化，以保证及时和正确的行为，同时允许在运行时重新配置调度参数。在本文中，我们考虑实现自主机器人计算意识这一目标的必要的第一步：从资源管理角度对一组基本计算内核进行实证研究。具体来说，我们对三个嵌入式计算平台上用于定位和映射、路径规划、任务分配、深度估计和光流的内核的时序、功耗和内存性能进行了数据驱动的研究。我们对这些内核进行概要分析，以深入了解计算感知型自主机器人的调度和动态资源管理。值得注意的是，我们的结果表明，内核性能与机器人的操作环境存在相关性，证明了计算感知机器人的概念，以及为什么我们的工作是实现这一目标的关键一步。
The potential impact of autonomous robots on everyday life is evident in emerging applications such as precision agriculture, search and rescue, and infrastructure inspection. However, such applications necessitate operation in unknown and unstructured environments with a broad and sophisticated set of objectives, all under strict computation and power limitations. We therefore argue that the computational kernels enabling robotic autonomy must bescheduledandoptimizedto guarantee timely and correct behavior, while allowing for reconfiguration of scheduling parameters at runtime. In this paper, we consider a necessary first step towards this goal ofcomputational awarenessin autonomous robots: an empirical study of a base set of computational kernels from the resource management perspective. Specifically, we conduct a data-driven study of the timing, power, and memory performance of kernels for localization and mapping, path planning, task allocation, depth estimation, and optical flow, across three embedded computing platforms. We profile and analyze these kernels to provide insight into scheduling and dynamic resource management for computation-aware autonomous robots. Notably, our results show that there is a correlation of kernel performance with a robot’s operational environment, justifying the notion of computation-aware robots and why our work is a crucial step towards this goal.