Welding Scan Pose Planning for Large-Object Seam Detection
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Solution Overview
Problem
Conventional robotic assembly systems face inefficiencies in scanning large objects due to the generation of massive, potentially useless scan data, slow processing times, and inability to accurately locate seams on objects exceeding a cubic meter in volume, leading to resource wastage and inefficiency.
Innovation Solution
A robotic system that uses a controller to identify regions associated with seams by selecting optimal scan poses based on candidate poses, evaluating their informational content and ease of assembly operation, and performing discrete or continuous scan operations to generate actionable data for precise welding.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If brute force scanning is performed on large objects, then complete surface coverage is achieved, but massive quantities of useless scan data are generated
Solution Approach 1:
The patent divides the scanning task into segments by identifying and focusing only on regions likely to contain seams, rather than scanning the entire object surface. This is achieved through predictive modeling and region-of-interest identification, which segments the large object into manageable scanning zones, reducing data volume while maintaining seam detection accuracy.
Solution Approach 2:
The patent applies local quality by concentrating scanning resources on specific regions where seams are likely to occur, rather than uniformly scanning the entire object. The system adjusts scan density and quality based on local seam probability, generating high-quality data only where needed and reducing overall data volume.
2Measurement precision
If repetitive scanning is performed to locate seams, then seam identification is achieved, but processing time increases significantly
Solution Approach 1:
The patent performs preliminary actions by using predictive models and object characteristics to pre-identify regions likely to contain seams before actual scanning begins. This preliminary segmentation allows the system to skip unnecessary scanning areas, dramatically reducing processing time while maintaining accurate seam location identification.
Solution Approach 2:
The patent implements skipping by rapidly moving through or completely bypassing regions of the object surface that are unlikely to contain seams. The system uses predictive algorithms to identify and skip non-relevant areas, rushing through the scanning process only in regions where seam detection is necessary, thereby reducing overall processing time.
3Measurement precision
If conventional scanning approaches are used on objects exceeding cubic meter volume, then complete coverage is achieved, but the approach becomes infeasible
Solution Approach 1:
The patent applies segmentation by dividing large objects into multiple regions of interest based on predicted seam locations, allowing the scanning system to process only relevant segments. This approach makes scanning of cubic-meter-scale objects feasible by breaking down the overwhelming task into manageable, targeted scanning operations.
Solution Approach 2:
The patent uses partial action by performing scanning only on portions of the object surface where seams are likely to exist, rather than achieving complete surface coverage. The system applies excessive action in terms of predictive modeling and region identification to ensure that all potential seam locations are captured, even if not every surface point is scanned.
Data Source
AI summary
Disclosed are systems, methods, and apparatuses, including computer programs encoded on computer storage media, for operation of an assembly robotic system. In one aspect, the assembly robotic system performs at least one of a first or second scan operation. In the first scan operation, one or more scan poses is selected from among a plurality of generated candidate poses. For each scan pose of the one or more scan poses, the controller initiates a scan operation associated with a region identified to include a seam associated with a feature of the object. As part of the second scan operation, for each candidate scan pose, a scan operation is simulated. Based on the generated simulated scan data, multiple scan poses are selected and a scan trajectory is generated for a scan operation. Other aspects and features are also claimed and described.