Robot Handling Assembly With 3D Station Recognition for Fast Initialization
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Solution Overview
Problem
Conventional training methods for handling devices, such as industrial robots, require expert intervention and are time-consuming, especially for multi-axle robots, as they rely on manual positioning and complex programming, limiting efficient initialization and operation in dynamic working environments.
Innovation Solution
A handling assembly equipped with a multi-axle robot, 3D monitoring sensors, and a localization module that automatically recognizes stations and workpieces using machine learning-based recognition features, enabling rapid initialization and autonomous operation without the need for expert intervention, and includes modules for model production, control, safety, and path planning to ensure efficient workstep execution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual positioning and complex programming methods are used for training handling devices, then the handling device can be precisely controlled, but the training process becomes time-consuming and requires expert intervention
Solution Approach 1:
The patent replaces manual mechanical positioning with an optical monitoring system using cameras and image processing. The monitoring sensor captures images of the working region, and a control unit automatically determines robot positions and station locations from these images, eliminating the need for manual mechanical positioning while maintaining precision.
Solution Approach 2:
The system enables self-service training by allowing the robot to automatically learn station positions and working region characteristics through optical monitoring. The control unit processes images to autonomously determine positions without requiring expert intervention, making the training process self-directed and efficient.
2Reliability
If conventional training methods are used for multi-axle robots, then the robot can operate with precise control, but the initialization process becomes complex and time-consuming
Solution Approach 1:
The patent replaces complex mechanical training procedures with an optical-based automated recognition system. The monitoring sensor and control unit work together to automatically identify stations and determine positions, simplifying the training process while ensuring reliable operation through precise optical measurement and automated control.
3Ease of operation
If manual positioning methods are used, then the handling device can be trained, but expert intervention is required which reduces operational efficiency
Solution Approach 1:
The system enables self-service training by allowing the robot to automatically learn station positions and working region characteristics through optical monitoring. The control unit processes images to autonomously determine positions without requiring expert intervention, making the training process self-directed and efficient.
Solution Approach 2:
The patent replaces manual mechanical positioning with an optical monitoring system using cameras and image processing. The monitoring sensor captures images of the working region, and a control unit automatically determines robot positions and station locations from these images, eliminating the need for manual mechanical positioning while maintaining precision.
4Reliability
If conventional training methods are used, then the handling device can be initialized, but the process is time-consuming which limits efficient operation in dynamic environments
Solution Approach 1:
The patent replaces manual mechanical positioning with an optical monitoring system using cameras and image processing. The monitoring sensor captures images of the working region, and a control unit automatically determines robot positions and station locations from these images, eliminating the need for manual mechanical positioning while maintaining precision.
Solution Approach 2:
The system enables self-service training by allowing the robot to automatically learn station positions and working region characteristics through optical monitoring. The control unit processes images to autonomously determine positions without requiring expert intervention, making the training process self-directed and efficient.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Facilitates the rapid and efficient initialization of multi-axle robots, allowing them to operate autonomously in dynamic environments by automatically recognizing stations and workpieces, reducing the need for expert intervention and enhancing safety through real-time monitoring and collision-free path planning.
Implementation Method 1
at least one monitoring sensor for the optical monitoring of the working region and for provision as monitoring data
Data Source
AI summary
A handling assembly having a handling device for carrying out at least one working step with and/or on a workpiece in a working region of the handling device, stations being situated in the working region, with at least one monitoring sensor for the optical monitoring of the working region and for provision as monitoring data, with a localization module, the localization module being designed to recognize the stations and to determine a station position for each of the stations.