End Effector Identification Data for Rapid Robot Retooling
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Solution Overview
Problem
The existing qualification process for robot-end effector pairs is lengthy and resource-intensive, requiring significant downtime and constraints robots to work only with specific types of end effectors, limiting agility and efficiency in production environments.
Innovation Solution
A modular processing system where the end effector stores identification data specific to its characteristics, allowing the robot to quickly identify and adjust runtime parameters upon attachment, thereby bypassing extensive calibration and qualification processes, enabling rapid retooling and operation with multiple types of end effectors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If a complex qualification process is used to ensure accurate robot-end effector pairing, then manufacturing precision and reliability are improved, but loss of time and productivity deteriorate significantly
Solution Approach 1:
The system performs preliminary actions by pre-measuring and storing compensation offsets and identification data in the end effector's memory during manufacturing. This allows the robot to quickly retrieve and apply these pre-prepared parameters during attachment, eliminating the need for time-consuming real-time qualification processes while maintaining accuracy.
Solution Approach 2:
The invention creates a digital copy of the end effector's physical characteristics by storing its identification data and compensation offsets in electronic memory. This digital replica allows the robot to instantly access and use the end effector's parameters without performing physical measurement and calibration, dramatically reducing configuration time while preserving precision.
2Reliability
If extensive calibration processes are performed to qualify robot-end effector pairs, then reliability is improved, but productivity and ease of operation worsen due to production line downtime
Solution Approach 1:
The end effector performs self-service by automatically providing its identification data and compensation offsets to the robot through its onboard memory. This eliminates the need for external calibration equipment and operators to perform time-consuming qualification processes, allowing rapid attachment and immediate production resumption while maintaining reliable operation.
Solution Approach 2:
All necessary calibration data and compensation parameters are prepared in advance during end effector manufacturing and stored in its memory. When the end effector is attached to the robot, these pre-prepared parameters are instantly transferred and applied, eliminating the need for production-line calibration activities and maximizing productivity.
3Manufacturing precision
If robots are configured to work with specific end effector types through extensive qualification, then manufacturing precision is improved, but adaptability deteriorates as robots cannot easily switch between different end effector types
Solution Approach 1:
The system achieves universality by implementing a standardized identification data structure and communication protocol that works across different end effector types. The robot uses a generic attachment and configuration process that automatically adapts to any end effector by reading its unique identification data from memory, enabling one robot to work with multiple end effector types while maintaining operation accuracy.
Solution Approach 2:
The invention enables rapid adaptation by changing operational parameters through electronic data transfer rather than physical reconfiguration. When a different end effector is attached, the robot automatically retrieves new identification data and compensation parameters from the end effector's memory, instantly adjusting its operation to match the new tool's characteristics without requiring requalification.
Data Source
AI summary
Technology identifies that an end effector is provisioned to a robot. The technology accesses identification data of the end effector. The identification data is specific to the end effector. The identification data includes one or more of at least one setting associated with the end effector or at least one parameter associated with the end effector. The technology controls the end effector based on the identification data to adjust one or more runtime parameters of the robot based on the identification data.


