AI Model Training for Robotic Fastener Path Planning
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
The challenge of uniformly removing aircraft fasteners due to their non-uniform arrangement across different aircraft models, requiring individualized path and trajectory planning for each fastener, complicates automated fastener removal systems.
Innovation Solution
A system using a user interface (UI) for guiding robots through dynamic path and trajectory planning, incorporating point cloud data segmentation, feature extraction, and machine learning models to accurately position and classify fasteners, enabling precise robotic operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If individualized path and trajectory planning is performed for each fastener, then manufacturing precision is improved, but device complexity increases
Solution Approach 1:
The system performs preliminary scanning and classification of fasteners before removal operations. Point cloud data is acquired and processed in advance to identify fastener locations, types, and orientations. This preliminary action enables the generation of customized paths and trajectories before the actual removal process, resolving the contradiction by preparing individualized plans beforehand rather than computing them in real-time during operation.
Solution Approach 2:
The system creates a digital replica or model of the aircraft structure with fastener positions and characteristics stored in a database. This digital copy allows for pre-computation and storage of removal paths and trajectories for different fastener configurations. When a specific fastener needs removal, the pre-planned path corresponding to that fastener's configuration is retrieved and executed, avoiding complex real-time calculations while maintaining precision.
2Adaptability or versatility
If dynamic path and trajectory planning is implemented, then adaptability is improved, but computation time increases
Solution Approach 1:
The system performs preliminary classification of fasteners based on their types, positions, and orientations using machine learning models. This classification is done in advance before the actual removal operation, allowing the system to adapt to different fastener configurations without performing complex computations during the time-critical removal process. The preliminary classification enables quick retrieval and execution of appropriate removal parameters.
Solution Approach 2:
The system employs dynamic path adjustment capabilities where the removal path and trajectory can be modified in real-time based on detected fastener characteristics. The system maintains a library of pre-computed paths for different fastener types and dynamically selects and adjusts the appropriate path during operation. This dynamic approach allows adaptability to various fastener arrangements while keeping computation time minimal by relying on pre-prepared path templates that can be quickly adjusted rather than computed from scratch.
3Measurement precision
If machine learning models are used for fastener classification, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The system introduces machine learning models as intermediary components between the point cloud data acquisition and the fastener removal execution. These models act as intelligent mediators that automatically classify fasteners based on their geometric and contextual features extracted from the point cloud data. The machine learning models translate complex point cloud information into simplified classification results that directly inform the removal process, improving measurement precision while managing system complexity through specialized intermediate processing layers.
Data Source
AI summary
Aspects of the disclosure are directed towards updated an object representation. An example method can include causing, based on a representation of a part of an object, a robot to position an image capturing device relative to the part, the representation indicating a first characteristic of the object. The method can further include receiving a first image generated by the image capturing device while the image capturing device is positioned relative to the part. The method can further include generating a first input to a machine learning model based on the first image. The method can further include determining a first output of the machine learning model based on the first input, the first output indicating a second characteristic different than the first characteristic. The method can further include generating an updated representation that indicates the second characteristic in place of the first characteristic.