Adaptive Human-Robot Collaboration for Masonry Construction Under Material and Assembly Uncertainty
For human-robot collaboration in construction, this work addresses communication and tolerance challenges with a practical system, though it is an incremental improvement over existing methods.
The paper presents an adaptive human-robot collaborative workflow for masonry construction that uses projection guidance and laser scanning to mitigate tolerance accumulation from material and assembly uncertainties. Experiments show projection guidance improves adhesive application consistency and reduces time, while laser-based correction maintains level courses and avoids failures.
Human-robot collaboration in construction is often challenged by limited robot-to-human communication and the need to adapt to tolerance accumulation arising from material and assembly uncertainties. We present an adaptive human-robot collaborative workflow for masonry construction that addresses communication limitations and tolerance accumulation, demonstrated through a brickwork case study in which a robot places bricks while a human applies adhesive. This workflow is enabled by two complementary mechanisms: 1) an end-effector-mounted projector that provides spatially registered, just-in-time projection guidance for manual adhesive application, and 2) laser scanning for feedback-driven grasping and placement pose correction. Together, these mechanisms enable adjustment of human and robotic actions in response to material variability and accumulated assembly tolerances. Full-scale experiments across conventional running-bond and nonstandard configurations demonstrate that projection guidance improves adhesive application consistency and reduces application time, while laser-based correction maintains level courses and avoids collision-prone failures associated with open-loop execution. These results indicate that integrating spatial projection with feedback-driven adaptation, enabled by material and as-built sensing, can mitigate tolerance accumulation and improve precision and robustness in human-robot collaborative construction.