Research on environment perception and behavior prediction of intelligent UAV based on semantic communication
Kechong Ren, Li Gao, Qi Guan

TL;DR
This paper presents a comprehensive approach combining reinforcement learning, semantic communication, and blockchain technology to enhance environment perception, behavior prediction, and secure data exchange in intelligent UAV systems, improving adaptability, efficiency, and security.
Contribution
It introduces a novel reinforcement learning method for UAV adaptation, a semantic communication framework for meta-universes, and a lightweight blockchain-based security scheme, integrating these for improved UAV environment perception and behavior prediction.
Findings
Drone adaptation performance improved by about 35%.
Local offloading rate reaches 90% with more base stations.
Semantic communication system outperforms baseline models.
Abstract
The convergence of drone delivery systems, virtual worlds, and blockchain has transformed logistics and supply chain management, providing a fast, and environmentally friendly alternative to traditional ground transportation methods;Provide users with a real-world experience, virtual service providers need to collect up-to-the-minute delivery information from edge devices. To address this challenge, 1) a reinforcement learning approach is introduced to enable drones with fast training capabilities and the ability to autonomously adapt to new virtual scenarios for effective resource allocation.2) A semantic communication framework for meta-universes is proposed, which utilizes the extraction of semantic information to reduce the communication cost and incentivize the transmission of information for meta-universe services.3) In order to ensure that user information security, a lightweight…
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Taxonomy
TopicsRobotics and Automated Systems · Cognitive Computing and Networks · Maritime Navigation and Safety
Methodstravel james · Balanced Selection
