3D Measurement Robot Trajectory Planning for Aircraft Surface Features
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Solution Overview
Problem
Traditional aircraft manufacturing relies on analog detection and human visual judgment for aerodynamic configuration measurement, leading to measurement accuracy issues and inefficiencies, which hinder digital manufacturing and lean production.
Innovation Solution
A feature-guided scanning trajectory optimization method for 3D measurement robots, involving the extraction of features from a 3D digital model, generation of initial scanning trajectories, optimization using a constraint model, and calculation of global optimal trajectories with a modified ant colony optimization algorithm.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional analog detection and human visual judgment are used for aerodynamic configuration measurement, then the measurement process is simple to implement, but the measurement accuracy is low and susceptible to human factors
Solution Approach 1:
The patent replaces traditional mechanical measurement methods and human visual judgment with a 3D measurement robot system that uses optical scanning technology. The robot equipped with a scanner automatically captures surface data points, eliminating human subjectivity and significantly improving measurement accuracy while providing objective, repeatable results
Solution Approach 2:
The measurement system performs self-service through automated feature extraction and trajectory optimization algorithms. The system automatically identifies geometric features, generates optimal scanning paths, and processes measurement data without requiring continuous human intervention, thereby maintaining high accuracy while simplifying operational complexity
2Productivity
If traditional analog detection methods are used, then the equipment is simple, but the measurement efficiency is unsatisfactory and cannot enable digital manufacturing
Solution Approach 1:
The patent replaces inefficient manual measurement processes with an automated 3D measurement robot system. The robot moves along pre-planned trajectories and automatically captures surface data, dramatically increasing measurement speed and efficiency while enabling seamless integration with digital manufacturing workflows
Solution Approach 2:
The system performs preliminary actions by pre-processing the 3D model to extract features and generate optimal scanning trajectories before actual measurement. This preparation work optimizes the measurement path in advance, allowing the robot to efficiently collect data without real-time human intervention, thereby大幅提升 measurement efficiency
3Productivity
If a global optimal scanning trajectory is planned for all features, then the overall measurement efficiency is maximized, but the complexity of trajectory optimization increases significantly
Solution Approach 1:
The patent segments the measurement task by first identifying and classifying geometric features, then generating initial scanning trajectories for each feature type separately. This segmentation allows the complex global optimization problem to be broken down into manageable sub-problems, reducing computational complexity while maintaining overall efficiency
Solution Approach 2:
The system applies local quality optimization by generating feature-specific scanning trajectories tailored to each geometric feature's characteristics. Different feature types receive customized scanning patterns optimized for their specific geometry, improving local measurement quality while the overall system maintains high productivity through coordinated execution
4Measurement precision
If the scanning trajectory is optimized considering robot constraints, then the measurement accuracy is improved, but the computation time for trajectory optimization increases
Solution Approach 1:
The patent segments the trajectory optimization into two stages: first generating initial trajectories for each feature independently, then performing global optimization considering robot constraints. This segmentation reduces the computational burden by breaking down the complex constrained optimization problem into simpler sub-problems that can be solved more efficiently
Solution Approach 2:
The system performs preliminary action by pre-generating feature-based scanning trajectories before applying robot constraint optimization. This preliminary trajectory generation provides a good initial solution that reduces the search space for subsequent optimization, thereby improving scanning accuracy while minimizing additional computation time
Data Source
AI summary
A feature-guided scanning trajectory optimization method for a 3D measurement robot, including: building a 3D digital model of an aircraft surface; obtaining a size of the 3D digital model; extracting features to be measured; classifying the features to be measured; calculating a geometric parameter of each type of features to be measured; generating an initial scanning trajectory of each type of features to be measured; building a constraint model of the 3D measurement robot; optimizing the initial scanning trajectory into a local optimal scanning trajectory; and planning a global optimal scanning trajectory of each type of features to be measured on the aircraft surface by using a modified ant colony optimization algorithm.