Robot Welding Instruction Generation From Human Operator Input
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Solution Overview
Problem
Conventional robotic welding systems require substantial manual effort and expertise to generate robot instructions, which is time-consuming and resource-intensive, especially with the shortage of qualified welding operators.
Innovation Solution
A process to automatically generate machine-readable robot instructions from human perceptible operator instructions, analyzing sequences of welding-type operations to convert them into robot instructions, thereby automating the process and reducing the need for manual programming.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If manual programming methods are used to generate robot instructions, then the robot can perform welding operations with high precision, but the process requires substantial manual effort and expertise, making it time-consuming and resource-intensive
Solution Approach 1:
The system captures human operator instructions through various input methods (manual entry, video recording, sensor data) and creates a digital copy that is then processed and converted into robot instructions. This copying approach allows the robot to replicate human welding techniques without requiring experts to manually program each movement, significantly reducing instruction generation time while maintaining welding precision.
Solution Approach 2:
The patent replaces the mechanical process of manual instruction programming with an automated processing system that uses sensors, cameras, and algorithms to capture and convert human operations into robot instructions. This substitution eliminates the need for manual, step-by-step programming while preserving the precision of human-performed welding operations.
2Manufacturing precision
If manual programming methods are used to generate robot instructions, then the robot can perform welding operations with high precision, but the process requires substantial manual effort and expertise
Solution Approach 1:
The system enables itself to generate robot instructions by automatically capturing human operator actions through sensors and cameras, processing the captured data through algorithms, and converting it into executable robot instructions without requiring external manual programming expertise. This self-service capability reduces both the complexity and the need for specialized knowledge in the instruction generation process.
Solution Approach 2:
The patent introduces an intermediary processing system that acts as a bridge between human operator instructions and robot execution. This intermediary layer captures human operations, processes them through automated algorithms, and translates them into robot-compatible instructions, thereby simplifying the overall process and reducing the complexity of direct manual programming.
3Productivity
If automated instruction generation is implemented, then time and resources are saved by converting human operator instructions into robot instructions, but the system requires sophisticated processing and analysis capabilities
Solution Approach 1:
The processing system is designed to handle multiple types of input data (manual entries, video recordings, sensor information) and process them through a unified algorithmic framework. This multi-functional approach allows the system to maintain high productivity across different input types while managing complexity through standardized processing routines rather than separate systems for each input method.
Data Source
AI summary
In some examples, robotic welding systems facilitate the “conversion” and/or “translation” of human perceptible operator instructions into machine readable robot instructions via a robot instruction generation process. The automatic generation of robot instructions via the robot instruction generation process has the potential to save substantial time and energy that would otherwise have to be invested by robot programmers and/or welding experts to manually generate the robot instructions. By following the generated robot instructions, the robot is able to perform the same assembly process that a human operator would perform by following the operator instructions. Additionally, because substantial time and/or effort has often been spent honing and/or improving the human perceptible operator instructions to ensure the human operator executes the welding-type operations efficiently and/or effectively, robot execution of robot instructions generated based on the operator instructions is likely to result in similarly efficient and/or effective welding-type operations and/or part assembly.