Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/155998
Title: Automatic re-planning of lifting paths for robotized tower cranes in dynamic BIM environments
Authors: Dutta, Souravik
Cai, Yiyu
Huang, Lihui
Zheng, Jianmin
Keywords: Engineering::Industrial engineering::Automation
Engineering::Civil engineering::Construction technology
Issue Date: 2020
Source: Dutta, S., Cai, Y., Huang, L. & Zheng, J. (2020). Automatic re-planning of lifting paths for robotized tower cranes in dynamic BIM environments. Automation in Construction, 110, 102998-. https://dx.doi.org/10.1016/j.autcon.2019.102998
Journal: Automation in Construction
Abstract: Computer-Aided Lift Planning (CALP) systems provide smart and optimal solutions for automatic crane lifting, supported by intelligent decision-making and planning algorithms along with computer graphics and simulations. Re-planning collision-free optimal lifting paths in near real-time is an essential feature for a robotized crane operating in a construction environment that is changing with time. The primary focus of the present research work is to develop a re-planning module for the CALP system designed at Nanyang Technological University. The CALP system employs GPU-based parallelization approach for discrete and continuous collision detection as well as for path planning. Building Information Modeling (BIM) is utilized in the system, and a Single-level Depth Map (SDM) representation is implemented to reduce the huge data set of BIM models for usage in discrete and continuous collision detection. The proposed re-planning module constitutes of a Decision Support System (DSS) and a Path Re-planner (PRP). A novel re-planning decision making algorithm using multi-level Oriented Bounding Boxes (OBBs) is formulated for the DSS. A path re-planning strategy via updating the start configuration for the local path is devised for the PRP. Two case studies are carried out with real-world models of a building and a specific tower crane to validate the effective performance of the re-planning module. The results show excellent decision accuracy and near real-time re-planning with high optimality.
URI: https://hdl.handle.net/10356/155998
ISSN: 0926-5805
DOI: 10.1016/j.autcon.2019.102998
Rights: © 2019 Elsevier B.V. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
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