Artificial intelligence has been adopted to facilitate monitoring, operation, and decision in the logistics field. Logistics robots with environment perception capability have been used to improve warehousing efficiency in logistics systems. However, autonomous mobile robots face computationally intensive and real-time demanding tasks such as navigation, localization, and obstacle avoidance. In this paper, we present EventTube, an edge computing-based event aware system that can efficiently discover events from the video data captured by RGB-Monoculars, and collaborate with individual devices to make timely decisions. EventTube deploys a semantic context extraction pipeline on edge servers to aggregate video streams from mobile robots and feed a few keyframes, including the start and end of the specific events to the successive perception pods, accelerating logistics robots' response speed. The event-related model parameters are trained and updated online on a server. The video data collected at the warehouse site for our mobile robots show that EventTube significantly improves parcel delivery efficiency without affecting regular deliveries.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.