Point Cloud Object Pickup With Neural Network Posture Selection
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Solution Overview
Problem
In scenarios with varying object types and forms, it is challenging to adaptively pick up objects with different details using existing automation control technologies.
Innovation Solution
An object pickup method that utilizes point cloud data processed by a neural network to obtain target pickup posture information and type information, enabling the pickup apparatus to adaptively select the appropriate pickup mode and posture for diverse objects.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional automation control technologies are used for object pickup, then the system structure remains simple, but the system cannot adaptively pick up objects with varying details such as different types and sizes
Solution Approach 1:
The patent replaces traditional mechanical control systems with a neural network-based intelligent system. The neural network processes point cloud data to automatically determine pickup postures and object types, substituting complex mechanical adjustment mechanisms with software-based adaptive control. This allows the system to handle varying object details without proportionally increasing mechanical complexity.
Solution Approach 2:
The patent introduces point cloud data as an intermediary between the sensor and the pickup apparatus. The point cloud data serves as a detailed digital representation of the object, allowing the neural network to analyze object characteristics and determine appropriate pickup strategies without direct mechanical interaction during the analysis phase.
2Adaptability or versatility
If multiple types of end effectors are disposed in the pickup apparatus to handle different object types, then the adaptability improves, but the device complexity increases
Solution Approach 1:
The patent makes the pickup apparatus universal by equipping it with multiple types of end effectors (grasping, suction, magnetic) that can handle different object types. The neural network selects the appropriate end effector based on object characteristics, allowing a single multi-functional apparatus to replace multiple specialized devices, thereby improving adaptability while managing complexity through intelligent selection rather than permanent complex configuration.
3Measurement precision
If neural network processing is used to determine pickup posture and object type, then the pickup accuracy for varying objects improves, but the computation time and processing complexity increase
Solution Approach 1:
The patent performs preliminary action by training the neural network in advance with large datasets of point cloud information. This pre-training allows the network to quickly process new objects during actual pickup operations, as the complex learning and pattern recognition have already been performed during the training phase. The trained model can then make rapid predictions with high accuracy.
Solution Approach 2:
The patent segments the pickup process into distinct phases: point cloud data acquisition, neural network processing for posture and type determination, and execution of pickup action. This segmentation allows optimization of each phase independently, particularly enabling parallel processing where possible and focusing computational resources on the most critical analysis tasks.
Data Source
AI summary
An object pickup method includes obtaining point cloud data about a target object. The method also includes obtaining target pickup posture information and type information of the target object that are obtained by processing the point cloud data via a neural network, where the target pickup posture information is used for describing a target pickup posture of a pickup apparatus for the target object. The method also includes controlling, based on the target pickup posture information and the type information, the pickup apparatus to pick up the target object. According to the method, objects with varying details, for example, of different types, can be adaptively picked up with reference to the neural network.