Multi-Agent Instruction Generation for Adaptive Manufacturing Control
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Solution Overview
Problem
Current manufacturing processes for complex systems like aircraft require significant human labor and manual oversight, are costly, and lack flexibility to adapt to unplanned processes or new environments, especially when producing one-off parts or assemblies.
Innovation Solution
A multi-agent autonomous instruction generation system using a centralized AI control agent that learns from various autonomous, semi-autonomous, and human systems to generate instructions for actor agents, including role assignments, platform control, and tool selection, leveraging an autoregressive bidirectional LSTM attention network for intelligent control and situational awareness.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If rules-based or expert knowledge systems are used for control, then manufacturing processes can be automated, but the systems require manual oversight and lack flexibility in new environments
Solution Approach 1:
The patent implements dynamic adaptability by enabling autonomous subsystems to learn and adjust their behavior in real-time based on environmental feedback. The system transitions from static rules-based control to dynamic adaptive control, where agents continuously update their policies through reinforcement learning to handle new situations without manual reprogramming.
Solution Approach 2:
The autonomous subsystems perform self-learning and self-adjustment through reinforcement learning mechanisms. The system serves itself by automatically improving its control strategies based on observed outcomes, eliminating the need for continuous manual oversight and adaptation while maintaining high levels of automation.
2Productivity
If autonomous subsystems are used in manufacturing, then labor costs are reduced, but the systems require significant manual oversight and coordination
Solution Approach 1:
The patent divides the manufacturing system into multiple autonomous subsystems, each capable of independent decision-making within its domain. This segmentation allows each subsystem to operate autonomously without requiring centralized coordination for every action, reducing the overall complexity of inter-process coordination while maintaining high productivity.
Solution Approach 2:
The patent introduces a communication and coordination layer that acts as an intermediary between autonomous subsystems. This intermediary enables efficient information exchange and task coordination without requiring direct human oversight, allowing multiple autonomous agents to work together seamlessly while reducing manual coordination requirements.
3Reliability
If traditional manufacturing systems are used, then processes are well-controlled, but they cannot adapt to unplanned processes or one-off production
Solution Approach 1:
The patent implements continuous feedback loops where autonomous subsystems monitor their own performance and environmental conditions in real-time. This feedback mechanism enables the system to detect unplanned processes or anomalies and automatically adjust control strategies to maintain reliability while adapting to new situations, including one-off production scenarios.
Solution Approach 2:
The system dynamically changes control parameters based on real-time observations and learned patterns. By adjusting parameters adaptively rather than relying on fixed control settings, the system maintains reliable process control while becoming capable of handling unplanned processes and custom production requirements without sacrificing stability.
Data Source
AI summary
Solutions are provided for multi-agent autonomous instruction generation for manufacturing. An example includes: generating, for a plurality of actor agents, a first set of instructions for performing manufacturing tasks, wherein the actor agents include a human actor accessing a user interface (UI), an autonomous actor having a first sensor, a semi-autonomous actor having a second sensor, and a non-autonomous actor having a third sensor; receiving, by a control agent from at least the plurality of actor agents, observation data regarding performance of the actor agents on the manufacturing tasks, wherein the control agent comprises an autoregressive bidirectional long-term short-term memory (LSTM) attention network; and based at least on the instructions and the observation data, generating further instructions for performing manufacturing tasks. The instructions include at least one of a role assignment, platform control, tool selection, and tool utilization.