Autonomous Welding Robot Seam Detection for Adaptive Path Planning
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Solution Overview
Problem
Conventional welding techniques are labor-intensive, inefficient, and struggle with precision and flexibility, particularly in low- or medium-volume manufacturing settings, where irregularities in parts and the need for skilled operators to avoid collisions and misalignments lead to inefficiencies and defective products.
Innovation Solution
A computer-implemented method for a welding robot that uses movable sensors to map the workspace and identify seams in 3D space, allowing for dynamic generation of welding instructions and path planning without prior information, enabling precise and accurate welding of multiple candidate seams while avoiding collisions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional welding techniques are used, then labor intensity is high and efficiency is low, but automation can be introduced to reduce labor intensity and improve efficiency
Solution Approach 1:
The welding robot system performs self-positioning and self-adjustment by automatically scanning the workspace, identifying seam locations, and generating welding paths without human intervention. The system uses sensors to detect actual part positions and autonomously adjusts welding parameters, enabling the robot to serve itself in adapting to variations in the work environment.
Solution Approach 2:
The patent replaces manual mechanical welding operations with an automated robotic system that uses computer vision and sensor-based detection. Instead of human operators visually locating seams and manually guiding welders, the system uses optical sensors and image processing to automatically identify and track seam positions, substituting mechanical human operation with automated sensing and control.
2Manufacturing precision
If skilled operators are used to avoid collisions and misalignments, then welding precision can be maintained, but the need for skilled operators increases complexity and reduces efficiency
Solution Approach 1:
The system continuously scans the workspace during operation and uses sensor feedback to detect actual seam positions, compare them with planned paths, and dynamically adjust welding parameters. This closed-loop feedback mechanism enables the robot to maintain high precision by automatically compensating for deviations, eliminating the need for skilled operators to manually correct misalignments.
Solution Approach 2:
The system performs preliminary scanning and identification of seam locations before welding begins. By pre-mapping the workspace and identifying potential collision risks and seam positions in advance, the system can plan optimal welding paths that avoid collisions and ensure precision, replacing the need for skilled operators to reactively adjust during welding.
3Speed
If rigid welding paths are used based on pre-programmed instructions, then welding speed can be maintained, but adaptability to part irregularities is reduced
Solution Approach 1:
The welding path is not fixed but dynamically adjusted during operation based on real-time sensor data. The system continuously updates the welding path to match actual seam positions detected during scanning, allowing the robot to maintain high speed while adapting to part irregularities. The welding parameters and trajectory are flexible and can be modified on-the-fly without reducing welding speed.
4Reliability
If manual welding operations are used, then flexibility to handle irregularities is available, but production time increases and reliability decreases
Solution Approach 1:
The robotic welding system operates continuously without interruption, performing scanning, path planning, and welding in an automated sequence. The system maintains continuous welding action along the seam without the stops, starts, and repositioning required in manual operations, thereby reducing production time while ensuring consistent, reliable weld quality through automated control.
Data Source
AI summary
In various examples, a computer-implemented method of generating instructions for a welding robot. The computer-implemented method comprises identifying an expected position of a candidate seam on a part to be welded based on a Computer Aided Design (CAD) model of the part, scanning a workspace containing the part to produce a representation of the part, identifying the candidate seam on the part based on the representation of the part and the expected position of the candidate seam, determining an actual position of the candidate seam, and generating welding instructions for the welding robot based at least in part on the actual position of the candidate seam.