Autonomous Welding Robot Seam Detection and Collision-Free 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 program welding robots lead to inefficiencies and defects.
Innovation Solution
A computer-implemented method for generating welding instructions that uses movable sensors to map the workspace and parts in 3D space, allowing for accurate identification and welding of seams without prior information, and dynamic path planning to avoid collisions, enabling precise and flexible welding operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional welding techniques are used with skilled operators programming robots, then welding operations can be performed, but the process becomes labor-intensive and inefficient
Solution Approach 1:
The welding robot system performs self-positioning and self-adjustment by using sensors to automatically detect seam locations and determine robot pose, eliminating the need for skilled operators to program welding paths. The system serves itself by autonomously adapting to part variations and irregularities.
Solution Approach 2:
The patent replaces manual programming and mechanical positioning with sensor-based detection and computational algorithms. Sensors capture images of the workspace and parts, and computer vision algorithms automatically determine seam locations and robot positioning, substituting mechanical programming operations with optical detection and digital processing.
2Manufacturing precision
If traditional welding methods are used, then welding can be performed, but precision and flexibility are compromised due to part irregularities
Solution Approach 1:
The welding system dynamically adapts to part variations by using sensors to detect actual seam locations and part geometries in real-time. The robot continuously adjusts its positioning and welding parameters based on detected irregularities, maintaining precision across different part configurations without requiring reprogramming.
Solution Approach 2:
The system employs sensor feedback to detect seam locations and part variations, then uses this information to automatically adjust welding operations. The feedback loop enables the system to compensate for part irregularities and maintain high welding precision across diverse part geometries.
3Ease of operation
If skilled operators program welding robots, then welding operations can be customized, but the process requires significant time and expertise
Solution Approach 1:
The system performs preliminary sensing and detection of seam locations and part geometries before welding begins. By pre-detecting and storing seam information through sensor scanning, the system eliminates the need for time-consuming manual programming while maintaining ease of operation for subsequent welding tasks.
Solution Approach 2:
The patent uses sensor-based copying of seam locations and part geometries into digital representations. Instead of manual programming, the system creates digital models of the workpiece features through optical scanning, which can then be used for automated welding path generation without requiring operator expertise.
4Productivity
If manual welding operations are performed, then flexibility in handling irregularities is possible, but labor intensity and costs increase
Solution Approach 1:
The welding robot autonomously handles part irregularities by using sensors to detect and adapt to variations in real-time. The system serves itself by automatically adjusting welding parameters and positioning without human intervention, maintaining high productivity while eliminating the downtime and costs associated with manual reprogramming or adjustment.
Data Source
AI summary
In some examples, an autonomous robotic welding system comprises a workspace including a part having a seam, a sensor configured to capture multiple images within the workspace, a robot configured to lay weld along the seam, and a controller. The controller is configured to identify the seam on the part in the workspace based on the multiple images, plan a path for the robot to follow when welding the seam, the path including multiple different configurations of the robot, and instruct the robot to weld the seam according to the planned path.