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In intelligent unmanned ground vehicle systems, decision-making algorithms often face challenges in adapting to dynamically changing environments, and their generalization capabilities may be limited. Most existing decision-making algorithms can only achieve robust results in the original scene, but when transferred to new scenes under same task, the algorithm performance drops sharply. Moreover, when deploying decision-making algorithms to unmanned ground vehicles, they often struggle to achieve performance comparable to computer simulations. To tackle this challenge, this paper proposes Scene Semantic Reconstruction for Unmanned Ground Vehicle Virtual-Real Integration(S2RU). S2RU decomposes the scene into abstract entities with object semantic information and then combines these entities using compositional neural radiance fields to enhance the capabilities of the UGV agent. This means the decision-making process is divided into two stages. In the first stage, concrete entities in the original perceptual information are mapped to abstract entities and transformed into scene semantic maps. In the second stage, decisions are made based on scene semantic maps. We have validated in both simulation and real-world environments, showcasing robust transferability between these environments and enabling cross-scene transfer for the same task and validate the usability, completeness and stability of S2RU.Results demonstrate that our methods improve success rate of a particular task across different scenes by at least 20% compared to other virtual-real integration methods.
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