Autonomous Machine Task Negotiation for Dynamic Capability Allocation
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Solution Overview
Problem
Current autonomous robots are limited by static task configurations, requiring specialized hardware and software for specific tasks, leading to high deployment costs and limited scalability, as well as a lack of practical mechanisms for dynamic capability adjustment based on collaborative environment needs.
Innovation Solution
Implementing a system where autonomous machines can dynamically allocate and negotiate tasks among themselves based on current capabilities and scenarios, enabling collaborative self-learning and self-organization to effectively partition subtasks in a dynamic environment with minimal human intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If specialized hardware and software are implemented for specific tasks, then task execution capability is improved, but device complexity and deployment cost increase
Solution Approach 1:
The patent implements a universal task management system where autonomous machines can dynamically allocate and negotiate multiple types of tasks among themselves. The system uses a standardized task description format and capability advertisement mechanism that allows machines with different specialized functions to cooperate on diverse tasks without requiring task-specific configuration for each machine pair.
Solution Approach 2:
The patent introduces dynamic task allocation and capability negotiation mechanisms where machines can adjust their task assignments in real-time based on current capabilities and environmental conditions. The system continuously monitors machine status and reassigns tasks dynamically, replacing static configuration with adaptive, runtime decision-making.
2Device complexity
If static task configurations are used, then device complexity is reduced, but adaptability to environmental changes deteriorates
Solution Approach 1:
The patent implements feedback mechanisms where autonomous machines continuously monitor their own capabilities and the status of other machines in the group. This feedback loop enables dynamic task reallocation when machines become unavailable or when new capabilities are detected, allowing the system to adapt to environmental changes while maintaining manageable complexity through automated decision-making.
Solution Approach 2:
The system enables autonomous machines to self-manage task allocation through capability advertisement and negotiation protocols. Machines automatically detect their own capabilities, advertise them to the group, and participate in task negotiation without requiring external configuration or control,实现ing self-service adaptation to changing conditions.
3Manufacturing precision
If manual configuration and support are provided, then manufacturing precision is improved, but productivity decreases
Solution Approach 1:
The patent implements self-service mechanisms where autonomous machines automatically perform task allocation, capability matching, and coordination without human intervention. The system uses automated protocols for task advertisement, capability negotiation, and dynamic reallocation, eliminating the need for manual configuration while maintaining accurate task assignment through algorithmic decision-making.
Solution Approach 2:
The patent introduces a standardized task management protocol as an intermediary layer between machines and tasks. This protocol handles the complexity of task allocation, capability matching, and coordination automatically, providing precise task assignment without requiring manual intervention. The intermediary protocol translates high-level task descriptions into coordinated actions among multiple autonomous machines.
Data Source
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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.