Machine Cluster Layout and Policy Adaptation for Collaborative Tasks

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Solution Overview

Problem

Existing technologies face challenges in effectively coordinating the operations of automated and autonomous machines within clusters to perform collaborative tasks efficiently, particularly in dynamic environments where real-time adjustments and policy learning are required.

Innovation Solution

Implementing a system that enables automated and autonomous machines to collaborate through advanced task performance models, utilizing machine learning and radio communication technologies to facilitate dynamic policy updates and adaptive planning, allowing machines to adjust their operations based on real-time data and environmental conditions.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Adaptability or versatility

If automated and autonomous machines operate with fixed operation policies in static environments, then system simplicity is maintained, but adaptability to dynamic environments deteriorates

Engineering Contradiction:

Improveadaptability to dynamic environmentsVSAvoidsystem complexity

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The patent implements dynamic operation policies that can be adjusted in real-time based on environmental conditions and task requirements. The system transitions from static, pre-programmed policies to dynamic policies that adapt through machine learning algorithms, allowing machines to respond flexibly to changing conditions while maintaining manageable complexity through modular architecture.

Inventive Principle:

Principle #15Dynamics

Solution Approach 2:

The autonomous machines perform self-learning and self-adjustment through embedded machine learning models. The system enables machines to automatically update their own operation policies based on observed environmental patterns and task outcomes, reducing the need for external reconfiguration and minimizing the complexity burden on operators.

Inventive Principle:

Principle #25Self-service

2Productivity

If real-time policy updates and adaptive planning are implemented, then task completion efficiency is improved, but computational resource requirements increase

Engineering Contradiction:

Improvetask completion efficiencyVSAvoidcomputational resource consumption

Core Design Contradiction:

ProductivityVSUse of energy by moving object

Solution Approach 1:

The system performs preliminary learning and policy optimization during idle periods or between tasks. By pre-computing policy adjustments when computational resources are abundant, the system reduces the need for intensive real-time computation during task execution, thereby improving task completion efficiency while managing energy consumption through temporal distribution of computational loads.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The patent implements partial policy updates that focus only on the specific aspects of operation policies relevant to current environmental changes rather than complete re-computation. This selective updating approach maintains task completion efficiency by addressing only necessary adjustments while reducing overall computational resource consumption compared to full policy re-optimization.

Inventive Principle:

Principle #16Partial or excessive action

3Productivity

If machines operate independently with individual task performance models, then system simplicity is maintained, but collaborative task performance deteriorates

Engineering Contradiction:

Improvecollaborative task performanceVSAvoidcoordination complexity

Core Design Contradiction:

ProductivityVSDevice complexity

Solution Approach 1:

The patent merges individual task performance models into a unified collaborative framework where machines share common knowledge bases and coordination protocols. By combining individual models with a centralized coordination layer that manages inter-machine interactions, the system achieves improved collaborative task performance while controlling coordination complexity through standardized communication interfaces and shared learning mechanisms.

Inventive Principle:

Principle #5Merging (Combining)

Solution Approach 2:

The system implements universal operation policies that can be applied across different machine types and task domains. By creating multi-functional policy frameworks that work across various collaborative scenarios, the system enhances collaborative task performance without proportionally increasing coordination complexity, as the same policy structures serve multiple purposes and machine configurations.

Inventive Principle:

Principle #6Universality (Multi-functionality)

Data Source

PatentEP4202591B1Devices and non-transitory computer-readable medium for a collaboration of automated and autonomous machines

Publication Date: 2025.10.01 INTEL CORP

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AI summary

A device may include a processor configured to determine a layout for a plurality of automated machine clusters to be deployed in an environment based on a plurality of operation policies and an input task, wherein each operation policy provides a policy to operate one or more automated machines of one of the plurality of automated machine clusters for a trained task based on one or more policy parameters. The processor may further be configured to adjust the one or more policy parameters of at least one of the plurality of operation policies based on the determined layout in the environment and the input task.