Genie: Smart ROS-based Caching for Connected Autonomous Robots
CoRR(2024)
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
the time on average and as much as 67
confidence in its object map considerably over time.
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