Robotic Assembly Position Correction Using Best-Fit Alignment
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Solution Overview
Problem
Robotic assembly operations in the production of transport structures, such as vehicles and aircraft, face challenges in achieving precise assembly due to errors in part positioning, which can compromise the safety and reliability of the final product.
Innovation Solution
A method and system for in-process assembly error correction, where a computing system instructs robots to adjust the positioning of parts based on target arrangements, using techniques like best-fit corrections and interference checks to minimize positional errors and ensure accurate assembly.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If robots perform assembly operations with standard positioning, then assembly speed is maintained, but positioning precision deteriorates due to accumulated errors
Solution Approach 1:
The system performs preliminary actions by measuring the actual positions of all parts before assembly and computing correction values in advance. The best-fit correction algorithm calculates the optimal transformation that minimizes positioning errors before the assembly operation begins, allowing robots to execute corrected positions without real-time computation delays during assembly.
Solution Approach 2:
The system implements feedback by using measurement data from actual part positions to compute correction values that are applied to subsequent assembly operations. The best-fit algorithm processes measured deviations and generates corrective transformations that feed back into the robot positioning system, continuously improving accuracy across multiple assembly cycles.
2Manufacturing precision
If high-precision positioning is enforced throughout assembly, then assembly accuracy is improved, but system complexity increases due to need for excessive measurement and correction mechanisms
Solution Approach 1:
The system applies local quality by focusing measurement and correction efforts only on critical assembly features rather than requiring universal high-precision mechanisms throughout the entire system. The best-fit correction selectively adjusts positions of parts based on their specific tolerance requirements and functional importance, applying precision where needed while accepting standard tolerances elsewhere.
Solution Approach 2:
The system changes parameters by transforming the coordinate system and positioning parameters through the best-fit correction algorithm. Instead of modifying physical hardware to achieve higher precision, the system mathematically transforms the positioning parameters to compensate for manufacturing variations, achieving improved accuracy through parameter adjustment rather than physical modification.
3Manufacturing precision
If correction calculations are performed for every part joining operation, then positioning accuracy is improved, but computational time and processing load increase
Solution Approach 1:
The system performs correction calculations as a preliminary action before the actual assembly operation begins. By computing the best-fit correction once based on measured part positions, the system avoids repeated computational iterations during assembly, reducing real-time processing requirements while maintaining high positioning accuracy.
Solution Approach 2:
The system merges multiple correction considerations into a single best-fit transformation calculation. Rather than separately computing corrections for each part or feature, the algorithm combines all positioning deviations into one comprehensive transformation that optimizes the overall assembly accuracy, reducing total computational time while maintaining precision.
Data Source
AI summary
In the present disclosure, methods, systems, and apparatuses for in-process assembly error correction are described. In various embodiments, a target arrangement of parts of an assembly may be obtained, with the target arrangement including a first target position of a first part, a second target position of a second part, and a third target position of a third part. The first part and the second part may be robotically joined based on the first target position and the second target position to obtain a first subassembly of the assembly, with the first subassembly having a first physical arrangement that includes the physical arrangement of the first and second parts after joining. The first physical arrangement may be fitted to the target arrangement to obtain a fitted first physical arrangement. The first subassembly and the third part may be robotically joined based on the fitted first physical arrangement.