Robotic Teleoperation Digital Twin Assembly With Precise Pose Alignment
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Solution Overview
Problem
Conventional digital twin modeling methods for robotic teleoperation environments face challenges in achieving precise and consistent geometric modeling due to issues with occlusions and the lack of semantic segmentation in vision-based reconstruction methods.
Innovation Solution
A digital twin modeling method that captures images of the teleoperation environment, identifies the parts being assembled, and generates high-precision three-dimensional models of the assembly and robot by determining their positional relationships, thereby avoiding dense vision-based reconstruction and improving accuracy and visualization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If vision-based reconstruction methods are used to build digital twin models, then the modeling process can be automated, but the measurement precision and manufacturing precision deteriorate due to occlusions and lack of semantic segmentation
Solution Approach 1:
The patent segments the assembly into multiple individual parts with unique identifiers, allowing each part to be independently modeled and tracked. This segmentation enables precise geometric representation of each component while maintaining automated modeling through systematic part identification and coordinate transformation, resolving the contradiction between automation and precision.
Solution Approach 2:
The patent introduces assembly constraint relationships as an intermediary mechanism that connects individual part models to form the complete assembly digital twin. These constraints serve as mediators that enable automated assembly reconstruction while maintaining high geometric precision by enforcing proper spatial relationships between parts, avoiding the need for direct vision-based reconstruction of the entire assembly.
2Loss of information
If vision-based dense reconstruction is used to create three-dimensional models, then complete coverage of the assembly can be achieved, but the measurement precision deteriorates due to occlusions
Solution Approach 1:
The patent performs preliminary action by pre-establishing assembly constraint relationships and coordinate transformation models before actual assembly reconstruction. This allows the system to predict and compensate for occlusion issues in advance, maintaining complete information coverage while ensuring high measurement precision through pre-defined geometric constraints rather than relying on potentially incomplete vision data.
Solution Approach 2:
The patent creates precise copies of individual part models with accurate geometric information, then assembles these copies according to constraint relationships. This copying approach ensures complete information representation without the occlusion problems of direct vision-based dense reconstruction, as each part copy maintains its full geometric details independently of viewing angles or occlusions.
3Adaptability or versatility
If conventional vision-based reconstruction is used, then the modeling process can handle dynamic environments, but the device complexity increases due to the need for complex reconstruction algorithms
Solution Approach 1:
The patent implements dynamics by enabling real-time updates of the digital twin model as assembly progresses. Individual part models and their coordinate transformations can be dynamically added or updated without reprocessing the entire assembly, allowing the system to adapt to dynamic assembly environments while keeping computational complexity manageable through incremental model building rather than complete reconstruction.
Solution Approach 2:
The patent segments the complex reconstruction task into independent part-level operations, where each part can be processed separately using simple coordinate transformation and constraint application. This segmentation reduces overall system complexity by breaking down the complex vision-based reconstruction into multiple simple, manageable operations that can be executed incrementally as assembly progresses.
Data Source
AI summary
A digital twin modeling method to assemble a robotic teleoperation environment, including: capturing images of the teleoperation environment; identifying a part being assembled; querying the assembly assembling order to obtain a list of assembled parts according to the part being assembled; generating a three-dimensional model of the current assembly from the list and calculating position pose information of the current assembly in an image acquisition device coordinate system; loading a three-dimensional model of the robot, determining a coordinate transformation relationship between a robot coordinate system and an image acquisition device coordinate system; determining position pose information of the robot in an image acquisition device coordinate system from the coordinate transformation relationship; determining a relative positional relationship between the current assembly and the robot from position pose information of the current assembly and the robot in an image acquisition device coordinate system; establishing a digital twin model of the teleoperation environment.