AI Tool Presetting Coordinate Recognition for Collision-Free Handling
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Solution Overview
Problem
Existing tool-presetting and tool-measuring systems lack operational reliability, efficiency, and have high operating costs due to inefficiencies in object recognition and navigation within the system.
Innovation Solution
Incorporating a trained machine-learning algorithm, particularly a CNN, into a control and regulation unit to perform coordinate recognition of tools, tool chucks, and tool chuck pallets, enabling precise determination of dimensions and positions, and optimizing gripper unit movements for collision-free and efficient operation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional object recognition methods are used in tool-presetting and tool-measuring systems, then the system structure remains simple, but operational reliability and measurement precision deteriorate
Solution Approach 1:
The patent replaces traditional mechanical or rule-based object recognition methods with a machine-learning algorithm (specifically a convolutional neural network CNN). This substitution enables the system to automatically learn and recognize tool objects, their types, and positions from camera images, significantly improving operational reliability and measurement precision while the system manages the increased computational complexity through integrated processing.
2Productivity
If manual tool presetting and measuring operations are performed, then system complexity remains low, but productivity and operational efficiency deteriorate
Solution Approach 1:
The patent implements self-service through automated machine-learning-based object recognition and coordinate determination. The system automatically identifies tools, tool chucks, and pallets in camera images, determines their coordinates in the fixed coordinate system, and enables autonomous navigation of the gripper unit, eliminating the need for manual operations and significantly improving productivity and operational efficiency.
Solution Approach 2:
The patent changes the operational parameters from manual control to automated control based on machine-learning algorithm outputs. The system processes camera images to extract object parameters (coordinates, positions, orientations) and uses these parameters to automatically control the gripper unit's movement and positioning, thereby increasing operational efficiency and productivity.
3Measurement precision
If precise coordinate recognition is implemented, then measurement precision and navigation accuracy improve, but processing time and computational requirements increase
Solution Approach 1:
The patent applies preliminary action by pre-training the convolutional neural network algorithm before actual operation. The training phase prepares the model to rapidly and accurately recognize tool objects and determine their coordinates during actual measurement and presetting operations. This preliminary preparation enables the system to achieve high measurement precision during operation without excessive processing time, as the heavy computational work of learning patterns has already been completed during training.
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
Enhances operational reliability, increases efficiency, reduces operating costs, and improves navigation accuracy by leveraging machine-learning for precise object recognition and path optimization.
Implementation Method 1
having at least one camera which is at least configured to capture camera images of a tool-presetting and/or tool-measuring region
Data Source
AI summary
A tool-presetting and/or tool-measuring system has an optical tool-presetting and/or tool-measuring apparatus, has at least one camera which is at least configured to capture camera images of a tool-presetting and/or tool-measuring region of the tool-presetting and/or tool-measuring apparatus and/or of a tool-storage, tool-retrieval or tool-intermediate-storage region of the tool-presetting and/or tool-measuring system, and has an, in particular external or internal, control and/or regulation unit which is at least configured to store and evaluate the camera images at least temporarily,wherein the control and/or regulation unit comprises a trained machine-learning algorithm which is at least configured to carry out, on the basis of the evaluated camera images, a coordinate recognition which comprises a recognition of tools, tool chucks, complete tools and/or tool and/or tool chuck pallets and a determination of the coordinates thereof in a fixed coordinate system.