Adaptive Human-Robot Collaboration for Masonry Construction Under Material and Assembly Uncertainty

TL;DR

This paper introduces an adaptive human-robot collaboration workflow for masonry construction that uses spatial projection and laser feedback to handle material variability and improve precision.

Contribution

It presents a novel workflow combining projection guidance and laser feedback for adaptive masonry assembly, addressing communication and tolerance challenges.

Findings

Projection guidance improves adhesive application consistency.

Laser-based correction maintains level courses and prevents collisions.

The approach enhances robustness in human-robot masonry tasks.

Abstract

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.