Intelligent Construction Robot System for Autonomous Building Model Generation
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Solution Overview
Problem
Current Building Information Modeling (BIM) technologies fail to accurately integrate building environment parameters with actual construction environment parameters, leading to inefficiencies and errors in construction automation, as construction robots require continuous monitoring and correction by technicians.
Innovation Solution
An intelligent construction robot system that captures point cloud data and building information data through machine learning to generate a building model, allowing for real-time evaluation and adaptation of construction schedules, enabling autonomous operation and reducing labor costs by integrating a learning module for machine learning and a judging module for similarity evaluation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If BIM technology is used to simulate building information, then building modeling capability is improved, but accuracy of construction environment parameters deteriorates
Solution Approach 1:
The patent merges BIM technology with point cloud recognition technology to create an integrated system. The learning module combines building information from BIM with actual construction environment data from point cloud scans, merging virtual modeling capabilities with real-world measurement accuracy to resolve the contradiction between ease of manufacture and measurement precision.
Solution Approach 2:
The patent introduces point cloud data as an intermediary between BIM models and actual construction environments. The point cloud serves as a mediator that captures real-world geometry and parameters, bridging the gap between simulated BIM data and actual construction site conditions, thereby improving parameter accuracy while maintaining modeling efficiency.
2Extent of automation
If construction robots operate autonomously using BIM data, then automation level is improved, but construction accuracy deteriorates
Solution Approach 1:
The patent implements a feedback mechanism where the robot captures point cloud data of the actual construction environment, compares it with BIM model data through the learning module, and uses the judging module to evaluate discrepancies. This feedback loop allows the autonomous robot to detect and correct deviations, maintaining high automation while ensuring construction accuracy through continuous verification against real-world measurements.
Solution Approach 2:
The patent performs preliminary action by pre-training the learning module with both BIM data and point cloud data before actual construction operations. This preliminary training enables the robot to anticipate and adapt to real-world variations, improving construction accuracy while maintaining autonomous operation without requiring real-time human intervention.
3Manufacturing precision
If technicians monitor and correct robot operations in real time, then construction accuracy is improved, but labor cost increases
Solution Approach 1:
The patent enables the construction robot to perform self-service through autonomous operation. The robot independently captures point cloud data, the learning module automatically processes and compares this data with BIM models, and the judging module autonomously evaluates whether to generate new building models. This self-service capability maintains construction accuracy through automated verification while eliminating the need for continuous technician monitoring, thereby reducing labor costs.
4Measurement precision
If point cloud data is integrated with BIM data through machine learning, then accuracy of building models is improved, but system complexity increases
Solution Approach 1:
The patent segments the complex system into distinct functional modules: a learning module that handles machine learning and data integration, a judging module that evaluates similarity and makes decisions, and a robot controller that executes commands. This segmentation manages system complexity by organizing complex functions into separate, manageable components while maintaining high building model accuracy through their coordinated operation.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The system effectively generates accurate building models that reflect actual construction environments, enabling autonomous operation, reducing preliminary preparation time, and improving construction efficiency by allowing multiple robots to work together with real-time monitoring and correction, thus enhancing overall construction quality and reducing error rates.
Implementation Method 1
the total station uses an optical path to obtain an absolute point of the environmental reflector corresponding to the building model; and at least one dynamic reflector is installed at any place on the construction robot, the total station uses the principle of the optical signal, the absolute position, and the absolute point to obtain a relative point of the dynamic reflector corresponding to the building model
Data Source
AI summary
An intelligent construction robot system having a learning module that performs machine learning based on point cloud data and building information data during a machine learning phase to generate a building model and store it; and a judging module that evaluates the similarity between the point cloud data and each of the building models during a machine interpretation phase to determine whether to use the building model with the highest similarity or to generate another building model, and the judging module generates a construction schedule corresponding to the building model.