[논문]Scene understanding for adaptive manipulation in robotized construction work

Lundeen, Kurt M.; Kamat, Vineet R.; Menassa, Carol C.; McGee, Wes

doi:10.1016/j.autcon.2017.06.022

Scene understanding for adaptive manipulation in robotized construction work 원문보기

Automation in construction, v.82, 2017년, pp.16 - 30

Lundeen, Kurt M. (Department of Civil and Environmental Engineering, University of Michigan, 2350 Hayward Street, 2340 G.G. Brown Building, Ann Arbor, MI 48109, United States) , Kamat, Vineet R. (Department of Civil and Environmental Engineering, University of Michigan, 2350 Hayward Street, 2340 G.G. Brown Building, Ann Arbor, MI 48109, United States) , Menassa, Carol C. (Department of Civil and Environmental Engineering, University of Michigan, 2350 Hayward Street, 2340 G.G. Brown Building, Ann Arbor, MI 48109, United States) , McGee, Wes (Taubman College of Architecture and Urban Planning, University of Michigan, 2000 Bonisteel Boulevard, Ann Arbor, MI 48109, United States)

Abstract ▼ AI-Helper

Abstract Unlike manufacturing robots, whose kinematics are pre-programmed based on robust metrology, tight tolerances, and rigid workpieces, construction robots operate under conditions of imperfect metrology, loose tolerances, and large workpiece uncertainties. Despite having access to a designed Building Information Model (BIM), construction robots must sense and model their actual environment, and adapt their kinematic plan to compensate for deviations from the expected. This research investigates methods to enable the autonomous sensing and modeling of construction objects so construction robots can ultimately adapt to unexpected circumstances and perform quality work. To that end, two construction component model fitting techniques are presented, namely the Clustering and Iterative Closest Point (CICP) construction component model fitting technique and the Generalized Resolution Correlative Scan Matching (GRCSM) construction component model fitting technique. The GRCSM construction component model fitting technique employs the presented GRCSM search algorithm, which is a modified version of the existing Multi-Resolution Correlative Scan Matching (MRCSM) search algorithm. Three experiments are presented to evaluate the ability of the CICP and GRCSM construction component model fitting techniques to model construction features. It was found that the CICP and GRCSM construction component model fitting techniques are capable of estimating the pose and geometry of arbitrarily shaped objects and construction joints, but are susceptible to modeling error. Despite their limitations, the CICP and GRCSM construction component model fitting techniques appear to be promising tools for the geometric estimation of construction features, especially for situations involving full automation, detailed construction work, incomplete sensor data, and complex object geometry. Highlights Key insight is provided into the need for construction robot scene understanding. A modified search algorithm GRCSM, a generalization of MRCSM, is introduced. Two construction component model fitting techniques, CICP and GRCSM, are introduced. The techniques are evaluated on an arbitrary object, virtual joint, and real joint. Techniques appear promising for the geometric estimation of construction components.

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Scene understanding for adaptive manipulation in robotized construction work 원문보기

Abstract ▼ AI-Helper

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