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Adaptive Visual Perception for Robotic Construction Process: A Multi-Robot Coordination Framework

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arxiv 2412.11275 v1 pith:JHIS3H7H submitted 2024-12-15 cs.RO

classification cs.RO
keywords constructionrobotsvisualcamerasoperationperceptionrobotviewpoint
verification ladder T0 review T1 audit T2 compute T3 formal
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Construction robots operate in unstructured construction sites, where effective visual perception is crucial for ensuring safe and seamless operations. However, construction robots often handle large elements and perform tasks across expansive areas, resulting in occluded views from onboard cameras and necessitating the use of multiple environmental cameras to capture the large task space. This study proposes a multi-robot coordination framework in which a team of supervising robots equipped with cameras adaptively adjust their poses to visually perceive the operation of the primary construction robot and its surrounding environment. A viewpoint selection method is proposed to determine each supervising robot's camera viewpoint, optimizing visual coverage and proximity while considering the visibility of the upcoming construction robot operation. A case study on prefabricated wooden frame installation demonstrates the system's feasibility, and further experiments are conducted to validate the performance and robustness of the proposed viewpoint selection method across various settings. This research advances visual perception of robotic construction processes and paves the way for integrating computer vision techniques to enable real-time adaption and responsiveness. Such advancements contribute to the safe and efficient operation of construction robots in inherently unstructured construction sites.

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Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Integrating LLMs and Digital Twins for Adaptive Multi-Robot Task Allocation in Construction

    cs.RO 2025-06 conditional novelty 5.0 of 10

    The paper integrates digital twins, integer programming, and LLMs so that natural-language site updates can automatically adapt multi-robot construction task allocation.

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