Grip Point Selection Between Shape Analysis and AI Models
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Solution Overview
Problem
Existing transfer devices face inefficiencies in gripping and transferring objects due to the need for either complex calculations based on exterior shape data or reliance on pre-trained models, which can reduce transfer efficiency and accuracy.
Innovation Solution
A selection device that chooses between two methods for determining a grip point based on object data, using either calculations from exterior shape data or outputs from a pre-trained model, to optimize the gripping method for each object's characteristics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If exterior shape data calculations are used to determine grip points, then accuracy is improved, but processing time increases and transfer efficiency decreases
Solution Approach 1:
The system dynamically switches between two grip point determination methods (exterior shape data calculation and pre-trained model) based on object characteristics. For objects where high accuracy is critical, the exterior shape method is used; for time-sensitive applications, the pre-trained model provides faster results. This dynamic adaptation resolves the contradiction between accuracy and processing time.
Solution Approach 2:
The system changes the processing parameters by selecting different computational approaches based on object properties. When objects have regular shapes, the pre-trained model provides sufficient accuracy with faster processing. When objects have complex geometries requiring precise grip points, the exterior shape data calculation is activated. This parameter-based selection optimizes the balance between accuracy and time.
2Device complexity
If a single method is used for all objects, then device complexity is reduced, but adaptability to different object characteristics decreases
Solution Approach 1:
The system achieves universality by implementing multiple grip point determination methods within a single system. The selection device can choose between exterior shape data calculation and pre-trained model based on object characteristics, making the system adaptable to diverse object types while maintaining manageable complexity through automated method selection.
Solution Approach 2:
The grip point determination process is segmented into different methods suitable for different object categories. Regular-shaped objects are handled by the pre-trained model, while objects with complex geometries are processed using exterior shape data calculation. This segmentation allows the system to maintain simplicity for common cases while providing specialized handling when needed.
3Reliability
If exterior shape data calculation is used, then reliability for complex objects is improved, but productivity decreases due to longer processing time
Solution Approach 1:
The system dynamically adjusts the determination method based on real-time object characteristics. For complex objects where reliability is paramount, exterior shape data calculation is selected. For simple objects where speed is more important, the pre-trained model is used. This dynamic adjustment optimizes the balance between reliability and productivity for each specific object.
Solution Approach 2:
Different determination methods are applied locally based on object characteristics rather than using a single global approach. The selection device evaluates object properties and applies the most appropriate method locally, ensuring high reliability for complex objects while maintaining overall system productivity through efficient handling of simpler objects.
Data Source
AI summary
According to one embodiment, a selection device selects an acquisition method of a grip point for gripping an object. Based on object data corresponding to a characteristic of the object, the selection device selects one of a first method or a second method. In the first method, the selection device calculates exterior shape data of the object from an image of the object, and calculates the grip point by using the exterior shape data. In the second method, the selection device inputs the image to a first model that is trained and acquires the grip point output from the first model.