Autonomous Factory Routing With Local Feedback Between Adjacent Systems
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Solution Overview
Problem
Existing solutions, including single-agent Reinforcement Learning (RL) and direct multi-agent Reinforcement Learning (Marl), fail to achieve optimal routing and processing of workpieces in autonomous factories due to limitations in modularity, scalability, and the requirement for global information.
Innovation Solution
A decentralized multi-agent Reinforcement Learning (Marl) approach with Independent Learning (IL) variant is implemented, where each system uses local information to adapt its machine learning model, allowing for modular, scalable, and heterogeneous system compositions without requiring global information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If decentralized multi-agent reinforcement learning with Independent Learning variant is used, then modularity and scalability are improved, but solution quality deteriorates with increasing number of systems
Solution Approach 1:
The autonomous factory is divided into independent plants, each with its own machine learning model that learns locally. This segmentation allows each plant to be trained and deployed independently, improving modularity and scalability while maintaining acceptable solution quality through local optimization
Solution Approach 2:
Each plant uses local information from its environment to train its machine learning model, rather than requiring global information. This local quality approach enables decentralized learning where each system adapts to its specific context, improving both modularity and maintaining solution quality through localized optimization
2Adaptability or versatility
If decentralized multi-agent reinforcement learning with Independent Learning variant is used, then system heterogeneity and scalability are improved, but training efficiency deteriorates due to linear scaling
Solution Approach 1:
Training is segmented into independent plant-level processes rather than centralized training. Each plant trains its own machine learning model using local data, which allows parallel training execution and reduces the linear time penalty associated with increasing system size
Solution Approach 2:
Each plant independently trains and optimizes its own machine learning model using local environmental information, without requiring centralized coordination or global information. This self-service approach enables autonomous adaptation and reduces training overhead as the system scales
3Reliability
If direct multi-agent reinforcement learning is used, then coordination between systems is improved, but computational complexity and information requirements worsen
Solution Approach 1:
The complex coordination requirements of direct multi-agent reinforcement learning are extracted and replaced with simpler local learning processes. Each plant independently learns from local feedback signals, eliminating the need for complex inter-agent communication and coordination mechanisms while maintaining system reliability
Data Source
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AI summary
The invention relates to a method for operating an autonomous factory (10) with at least two adjacent systems (12, 14), comprising the steps of: providing a first system (12) with a first production plan (24), wherein the first system (12) includes a first machine learning model (20); providing a second system (14) with a second production plan (26), wherein the second system (14) includes a second machine learning model (22), and wherein the second system (14) is adjacent to the first system (12); transmitting a workpiece (18) from the first system (12) to the second system (14); generating a feedback message (38) by means of the second system (14); and transmitting the feedback message (38) from the second system (14) to the first system (12) by means of a communication device (48) of the second system (14). The invention further relates to a computer program product, a computer-readable storage medium, and an autonomous factory (10).