Genie：面向联网自主机器人的基于ROS的智能缓存
Genie: Smart ROS-based Caching for Connected Autonomous Robots
AI总结：
针对自主机器人边缘计算存在的服务器分布不足、性能增益有限、与ROS不兼容等问题，本文提出基于ROS的非侵入式分布式智能缓存方案Genie，可显著降低自动驾驶任务延迟并提升目标数据质量。
摘要：
尽管自主机器人前景广阔，但目前仍存在若干关键问题可能导致性能与安全性下降。延迟便是其中之一：我们发现，即便是NVIDIA最新的嵌入式平台，也无法实时执行自主车辆的智能任务（例如目标检测）。解决这一问题的一种可行方案是前景广阔的边缘计算范式。通过与行业合作伙伴协作，我们明确了当前边缘思路的三大核心局限：（1）服务器分布不足，因此与车辆的距离不够近；（2）当前提出的边缘解决方案未能提供显著更优的性能，也未提供自主车辆专属的额外信息，不足以证明其对用户的成本合理性；（3）现有最优方案与自主系统常用的主流框架不兼容，尤其是机器人操作系统（Robot Operating System，ROS）。\n为解决这些问题，我们提出了Genie，这是一种封装技术，能够以非侵入式方式（即无需修改源代码）在ROS中实现透明缓存，能够以分布式方式构建缓存（与传统的集中式缓存方法不同），还能构建集体三维目标地图，从而为特定区域内的所有车辆大幅降低延迟（即便在低功耗边缘服务器上也能实现）并提供更高质量的数据。我们采用业内主流的自动驾驶软件Autoware，在当前行业采用的先进嵌入式与边缘平台上完整实现了我们的设计，实验结果表明：Genie可将Autoware视觉检测器的延迟平均降低82%，目标复用率平均可达31%，对传入请求的目标复用率最高可达67%，并且其目标地图的置信度会随时间推移显著提升。
英文摘要：
Despite the promising future of autonomous robots, several key issues currently remain that can lead to compromised performance and safety. One such issue is latency, where we find that even the latest embedded platforms from NVIDIA fail to execute intelligence tasks (e.g., object detection) of autonomous vehicles in a real-time fashion. One remedy to this problem is the promising paradigm of edge computing. Through collaboration with our industry partner, we identify key prohibitive limitations of the current edge mindset: (1) servers are not distributed enough and thus, are not close enough to vehicles, (2) current proposed edge solutions do not provide substantially better performance and extra information specific to autonomous vehicles to warrant their cost to the user, and (3) the state-of-the-art solutions are not compatible with popular frameworks used in autonomous systems, particularly the Robot Operating System (ROS). To remedy these issues, we provide Genie, an encapsulation technique that can enable transparent caching in ROS in a non-intrusive way (i.e., without modifying the source code), can build the cache in a distributed manner (in contrast to traditional central caching methods), and can construct a collective three-dimensional object map to provide substantially better latency (even on low-power edge servers) and higher quality data to all vehicles in a certain locality. We fully implement our design on state-of-the-art industry-adopted embedded and edge platforms, using the prominent autonomous driving software Autoware, and find that Genie can enhance the latency of Autoware Vision Detector by 82% on average, enable object reusability 31% of the time on average and as much as 67% for the incoming requests, and boost the confidence in its object map considerably over time.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
