Robotic Arm Part Sorting Using Natural Language and AI Perception
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Solution Overview
Problem
Existing robotic arm control systems require high coding ability and are difficult to operate, limiting their use in flexible and safe industrial part sorting tasks, especially when parts need to be in fixed poses and the production line layout changes.
Innovation Solution
An embodied intelligence-based method using neural network models for task instruction understanding, intelligent perception, and obstacle-avoidance to automatically generate control signals for robotic arms, enabling accurate part sorting and obstacle avoidance without manual coding.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If users manually write task instruction codes for robotic arm control, then real-time control capability is achieved, but the difficulty of operation increases significantly
Solution Approach 1:
An instruction generation model serves as an intermediary between user natural language input and robotic arm control commands. The model translates simple text descriptions into complex control instruction sequences, eliminating the need for users to manually code while maintaining real-time control capability. This mediator handles the complexity transformation automatically.
Solution Approach 2:
The manual coding process is replaced by an AI-based instruction generation system. Instead of requiring users to mechanically write and debug control codes, the system uses natural language processing and machine learning models to自动生成 control instructions, substituting the manual mechanical coding process with an intelligent automated system.
2Manufacturing precision
If fixed pose requirements are imposed on parts, then sorting accuracy is improved, but the adaptability to production line changes decreases
Solution Approach 1:
The system transitions from static fixed-pose requirements to dynamic pose adaptation. The instruction generation model incorporates real-time vision system feedback to dynamically adjust control instructions based on actual part positions and orientations. This allows the system to maintain high sorting accuracy while adapting to varying part poses and production line layout changes without requiring parts to be in predetermined fixed positions.
3Reliability
If comprehensive control instructions are generated for sorting tasks, then the completeness of sorting function is improved, but the complexity of instruction generation increases
Solution Approach 1:
The instruction generation process is segmented into multiple specialized models working in sequence: a vision system for part recognition, an instruction generation model for control command creation, and an execution model for robotic arm control. Each segment handles a specific aspect of the sorting task, distributing the overall complexity across multiple specialized components rather than requiring one complex monolithic system.
Data Source
AI summary
The present application provides an embodied intelligence-based method and apparatus, a device and a medium for industrial part sorting processing. In this solution, firstly, a control signal for controlling a robotic arm to sort to-be-sorted parts is obtained by a task instruction understanding model based on part sorting description information inputted by a user, and then analysis processing is performed on an image of a to-be-sorted part by an intelligent perception model for parts according to the control signal to obtain a category and a grasp pose of the to-be-sorted part, the image being collected by an industrial camera. Finally, under obstacle-avoidance processing of an intelligent obstacle-avoidance neural network model, the robotic arm is controlled to sort the to-be-sorted part based on the control signal, the category and the grasp pose of the to-be-sorted part. This method helps reducing difficulty in controlling the robotic arm simply via natural language.