Human-Robot Collaborative Manufacturing Using AI Motion Primitives
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Solution Overview
Problem
Manufacturing industries face challenges with fixed-installation robots that lack flexibility and adaptability, requiring extensive programming and struggling to automate tasks in unfamiliar workspaces, often resorting to human labor for efficiency.
Innovation Solution
A human-robot collaborative system uses machine learning and AI to generate dynamic motion primitives from user-guided recordings, enabling flexible manufacturing by capturing images of a template workpiece and determining landmark locations, allowing robots to replicate manufacturing actions on subsequent workpieces without extensive programming.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If fixed-installation robots are used to improve production volume and quality, then manufacturing precision and productivity are improved, but adaptability and versatility deteriorate
Solution Approach 1:
The system transitions from static fixed-installation robots to dynamic mobile robots that can autonomously navigate and adapt to changing workspace configurations. The robot employs real-time sensor data and AI algorithms to dynamically adjust its position, orientation, and manufacturing actions, enabling both high precision and adaptability in the same system.
2Productivity
If extensive programming is implemented to achieve automated machinery operation, then productivity is improved, but device complexity and ease of manufacture worsen
Solution Approach 1:
The robotic system performs self-programming through autonomous learning from demonstrated human actions. The AI algorithm automatically generates control programs by analyzing sensor data from human demonstrations, eliminating the need for manual programming while maintaining high automation efficiency. The system learns task sequences, motion paths, and manipulation skills independently.
3Ease of operation
If human labor is used to maintain ease of operation for complex tasks, then ease of operation is maintained, but productivity and extent of automation worsen
Solution Approach 1:
The system replaces manual human operations with automated robotic actions guided by AI learning from human demonstrations. The robot captures essential operational patterns from human workers and autonomously executes manufacturing tasks, maintaining operational simplicity while dramatically increasing productivity through consistent, high-speed automation.
4Adaptability or versatility
If mobile robots with AI learning are deployed to improve adaptability, then versatility and ease of operation are improved, but device complexity worsens
Solution Approach 1:
The mobile robot is designed as a universal platform capable of performing multiple manufacturing functions through a single integrated system. It combines navigation, sensing, AI learning, and various end-effectors to handle diverse tasks including inspection, manipulation, and assembly, reducing overall system complexity while maximizing adaptability across different workspaces and tasks.
Data Source
AI summary
An exemplary method and system are disclosed to flexibly and adaptably manufacture and assemble a workpiece by using recordings of a user in machine learning/artificial intelligence algorithms to train a robot for subsequent automated manufacture. Machine learning and artificial intelligence learning can generate libraries of generalized dynamic motion primitives that can be subsequently combined for any type of manufacturing or assembling activity. The exemplary method and system can flexibly generate a model of an existing workpiece as a template or primer workpiece that can then be used in conjunction with the DMP operations to fabricate subsequent workpieces.