3D Work Zone Process Control Using Markerless Tool Tracking
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Solution Overview
Problem
Existing process control systems for work tasks on objects in manufacturing lines face challenges in accurately recognizing and tracking different workpieces and tools in dynamic environments, particularly when objects move along complex paths or are partially obscured, leading to errors and inefficiencies.
Innovation Solution
The use of LIDAR or TOF sensors to create a 3D point cloud of the scene, allowing for the identification and tracking of objects without external markers, enabling robust and flexible process control by recognizing characteristic surfaces and edges, and determining the position and orientation of objects in six spatial degrees of freedom.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If markers are attached to tools and objects for position detection, then position and orientation can be determined accurately, but the device complexity and preparation time increase
Solution Approach 1:
The patent extracts the marker attachment requirement from the system by using natural geometric features of the workpiece and tool instead of external markers. The position detection system directly identifies characteristic points, lines, and surfaces that inherently exist on the objects, eliminating the need for additional marker components and their attachment processes.
Solution Approach 2:
The workpiece and tool serve themselves for position detection by utilizing their own geometric characteristics. The workpiece geometry itself provides the reference features for detection, eliminating the need for external marking systems. This self-service approach reduces system complexity while maintaining detection accuracy.
2Reliability
If markers are used for position detection, then tracking is reliable, but the method loses flexibility for different workpiece types
Solution Approach 1:
The position detection system achieves universality by detecting natural geometric features that exist on various workpiece types without requiring type-specific markers. The system can adapt to different workpiece geometries by identifying their characteristic features, making it versatile across multiple workpiece types while maintaining reliable tracking through consistent feature-based detection.
Solution Approach 2:
The system changes detection parameters dynamically based on the detected workpiece geometry. By adapting the detection algorithm to recognize different geometric configurations and characteristic features, the system maintains reliable tracking across varying workpiece types without requiring physical reconfiguration or type-specific markers.
3Measurement precision
If complex calibration procedures are performed for marker-based systems, then detection accuracy improves, but setup time and productivity decrease
Solution Approach 1:
The system performs preliminary detection of workpiece geometry and automatically establishes the detection framework before actual production begins. By pre-identifying characteristic features and setting up the coordinate system based on natural workpiece geometry, the system achieves high detection accuracy without requiring time-consuming manual calibration procedures during setup.
Solution Approach 2:
The workpiece itself provides the calibration reference through its inherent geometric features. The detection system automatically uses the workpiece geometry to establish its own coordinate system and detection parameters, eliminating the need for external calibration artifacts and complex calibration procedures, thereby improving setup efficiency while maintaining accuracy.
4Measurement precision
If external markers are used for position detection, then six degrees of freedom can be determined, but the system becomes less robust to environmental changes
Solution Approach 1:
The system extracts position detection capability from external markers and embeds it within the natural geometry of the workpiece and tool. By using intrinsic geometric features that are part of the objects themselves rather than external attachments, the system becomes more robust to environmental changes such as lighting variations, marker detachment, or contamination, while still achieving six degrees of freedom detection.
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
This approach allows for precise and accurate recognition of objects and tools in dynamic environments, enabling efficient process control, automatic detection of multiple objects, and flexible adaptation to changing conditions, reducing the need for complex calibration and external markers.
Implementation Method 1
LIDAR or TOF sensors to create a 3D point cloud of the scene
Implementation Method 2
LIDAR or TOF sensors to create a 3D point cloud of the scene
Data Source
Figure 1~2
AI summary
A method and a device for process control of work tasks using at least one tool (4) on at least one object (2) are described, wherein the work tasks are to be performed in 3D work zones (10) defined on the object (2). Using a position detection system (5), the position and orientation of the tool (4) in a higher-level coordinate system of the position detection system (5) and the position and orientation of the object (3) are optically determined over time. From the determined position and orientation of the object (2), the defined 3D work zones (10) on the object (2) are determined. By comparing these positions over time, it is determined when the tool (4) is located in one of the 3D work zones (10) on the object (2), and then the execution of the work task for the object (2) in the 3D work zone (10) is enabled, parameterized, and/or recorded.In particular, a LIDAR sensor or a TOF sensor is provided as the sensor (9), (Fig. 1).