Sebastian Montoya Zuluaga
Leveraging Edge Computing Resources on Computing-Intensive Robotic Tasks.
Rel. Fulvio Giovanni Ottavio Risso, Jacopo Marino, Daniele Cacciabue. Politecnico di Torino, Corso di laurea magistrale in Ingegneria Informatica (Computer Engineering), 2025
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Abstract
In the previous years the industry has pushed for infrastructure management on the cloud, this meaning centralized computational resources which are then leased on demand. Within this paradigm there’s another option which has become a trend: Edge computing. In edge computing the “cloud” resources are carried geographically closer to the end user, which presents the same benefits as cloud computing with the addition of lower latency. Robotics applications have historically handled the computational burden locally (onboard computers) which comes with its own weight and power restrictions. Edge computing comes forward as a tool to be leveraged given its ability to perform graphically and memory dependent tasks within the critical parameter of latency allowing almost real-time results.
This work demonstrates a practical implementation of adaptive edge computing in robotics by combining ROS 2 lifecycle management, efficient computer vision for stop sign detection, peer-to-peer Kubernetes networking via Liqo, and WebSocket-based offloading
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