Interpreting Spatial Instructions for Effective Human-Robot Communication in Construction Environments

When utilizing robots to perform tasks in construction sites, it is important to ensure efficient communication between humans and robots based on spatial instructions and the intuitive decision-making of the operator, in order to adapt flexibly to changes in the environment or alterations in plans.However, it is challenging to accurately conveying the intent of the operator to the robot in complex construction environments.This becomes particularly difficult when dealing with tasks involving objects with curvature or geometric patterns, which increase the complexity of robot trajectory generation.Therefore, this study proposes a method to estimate the robot trajectory from spatial instruction that include deviation, specifically for targets with various forms.It focuses on interpreting spatial instructions in circular, rectangular, and linear shapes commonly used in construction, and the proposed approach involves two steps: classifying the trajectory shapes from spatial instruction and fitting the shapes through regression analysis.The accuracy of this method was evaluated in a scenario where 8 participants collaborated with a robot to cut ceiling-mounted components using spatial instructions provided through a laser pointer.As a result, the proposed method achieved an average Root Mean Squared Error of 2.398mm, compared to 7.274mm for the conventional B-Spline method.These results suggest that the regression analysis-based approach to interpreting spatial instructions has the potential to improve safety and productivity in construction tasks that rely on design plans and layouts.