Collaborative Robot Vision Calibration for Lab Automation
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Solution Overview
Problem
The calibration and configuration of robots in life sciences equipment automation are tedious and time-consuming, especially when interacting with various processing equipment in dynamic work cells.
Innovation Solution
The implementation of collaborative robots with articulated robot actuators and vision systems that can autonomously adjust their configuration and interface with different workstations based on imaged vision targets, allowing for efficient handling and processing of lab samples across variable work locations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional mobile carts with robots are used to transfer samples between processing equipment, then sample transfer capability is achieved, but calibration and configuration becomes tedious and time-consuming
Solution Approach 1:
The robot system performs self-calibration and self-configuration by autonomously detecting workstation locations and interfaces, eliminating the need for manual calibration operations. The robot independently maps the work cell environment and adapts its parameters to interface with different processing equipment.
Solution Approach 2:
The system performs preliminary mapping and detection of workstation locations and interfaces before actual sample transfer operations begin. This advance preparation allows the robot to have calibration data ready, eliminating time-consuming calibration steps during operational phases.
2Adaptability or versatility
If portable robotic manipulation systems are used with static task locations, then reconfigurability is limited, but setup simplicity is maintained
Solution Approach 1:
The robot system transitions from static task location configurations to dynamic, adaptable positioning. The robot can autonomously adjust its operational parameters and navigate to various workstation locations within the work cell, enabling flexible task performance across multiple equipment pieces without fixed positioning requirements.
Solution Approach 2:
The robot is designed with universal interfacing capabilities that allow it to work with multiple types of processing equipment at various locations. A single robot unit can perform diverse tasks across different workstations by automatically adapting to each equipment's specific interface requirements.
3Measurement precision
If manual calibration of robots with processing equipment is performed, then interface precision can be achieved, but time and effort requirements increase significantly
Solution Approach 1:
Manual mechanical calibration operations are replaced with automated optical and sensor-based detection systems. The robot uses vision systems and sensors to automatically detect workstation locations, interfaces, and alignment parameters, substituting manual measurement and adjustment procedures with automated detection and calculation.
Solution Approach 2:
The system implements closed-loop feedback where the robot detects its position and the workstation interface characteristics, compares them against target parameters, and automatically adjusts its operational parameters to achieve precise alignment. This continuous detection-adjustment cycle ensures high precision without manual intervention.
Data Source
AI summary
A collaborative robot including: a movable base to movably position the collaborative robot at different variable work locations; an articulated robot actuator movable relative to the base to effect with a robot end effector a predetermined function corresponding to at least one workstation at the at least one work location, the at least one workstation having an undeterministic variable pose with respect to the at least one work location; a vision system connected to the articulated robot actuator to image a vision target connected to and corresponding uniquely to each of the at least one workstation; and a controller to determine from the image data the workstation pose relative to the movable base and automatically teach the articulated robot actuator the workstation pose so as to effect a predetermined deterministic interface, associated with a predetermined workstation function characteristic, between the workstation and robot end effector.