Autonomous Machine Task Negotiation for Dynamic Allocation
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Solution Overview
Problem
Current autonomous robots are limited by static task configurations, leading to high deployment costs and limited scalability, as they are specialized for specific tasks and lack the ability to dynamically allocate tasks based on current scenarios, resulting in inefficient operation and increased human intervention for configuration and re-configuration.
Innovation Solution
Implementing a system where autonomous machines can dynamically allocate and negotiate tasks among themselves based on their capabilities and the current scenario, enabling collaborative self-learning and self-organization, allowing for dynamic task distribution and adaptation to environmental changes with minimal human intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If autonomous robots are specialized for specific tasks with static configurations, then task execution reliability is improved, but adaptability to different scenarios deteriorates and deployment cost increases
Solution Approach 1:
The patent implements a multi-functional robot platform where a single robot can perform multiple different tasks through dynamic task allocation and configuration. The robot system includes various effectors and sensors that can be programmed and reconfigured to execute different task types, eliminating the need for separate specialized robots for each task while maintaining reliable execution through structured task management protocols
2Manufacturing precision
If autonomous robots are specialized for specific tasks, then task execution precision is improved, but device complexity and deployment cost increase
Solution Approach 1:
The system uses a unified robot platform with standardized components that can be configured for different tasks through software rather than hardware modifications. This reduces overall system complexity by avoiding duplication of specialized components across multiple robot types while maintaining task execution precision through structured control and validation protocols
3Stability of the object's composition
If static task configurations are used, then system stability is improved, but productivity and operational efficiency deteriorate due to inability to dynamically allocate tasks
Solution Approach 1:
The patent implements dynamic task allocation where robots can receive, accept, and transfer tasks in real-time based on their current state, capabilities, and environmental conditions. The task management system allows for dynamic reconfiguration of task assignments while maintaining system stability through structured communication protocols and state validation, enabling the system to adapt to changing conditions without losing operational coherence
4Reliability
If specialized autonomous robots are deployed for different tasks, then task execution reliability is improved, but loss of time for re-configuration and human intervention increases
Solution Approach 1:
The robot system implements self-service capabilities where robots can autonomously evaluate their own state, determine their suitability for tasks, and negotiate task allocations with other robots and the central system without requiring human intervention. The system includes automated capability assessment, task matching algorithms, and self-configuration protocols that enable robots to adapt to new tasks independently, significantly reducing re-configuration time while maintaining execution reliability
Data Source
AI summary
According to various aspects, controller for an automated machine may include: a processor configured to: compare information about a function of the automated machine with information of a set of tasks available to a plurality of automated machines; negotiate, with the other automated machines of the plurality of automated machines and based on a result of the comparison, which task of the set of tasks is allocated to the automated machine.