Multi-Robot Auction Control Under Uncertainty and Task Order
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Solution Overview
Problem
Efficient coordination of multiple robots in multi-agent systems is challenging, especially under uncertain conditions with chronological dependencies, as existing methods fail to effectively distribute tasks and handle uncertainties in real-time surroundings.
Innovation Solution
A method using a deterministic finite automaton to assign state transitions to robots through an auction process, considering action costs and experience parameters, allowing for decentralized task distribution and adaptation to changing conditions, enabling efficient handling of chronological dependencies and uncertainties.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional auction methods are used for task distribution, then task allocation can be achieved, but chronological dependencies and uncertainties in real-time surroundings cannot be effectively handled
Solution Approach 1:
The patent implements dynamic task distribution by allowing the auction mechanism to adapt in real-time based on chronological dependencies and environmental uncertainties. The system dynamically adjusts task allocations as new information becomes available, rather than using static pre-assigned tasks, enabling effective handling of time-dependent specifications and uncertain conditions.
Solution Approach 2:
The patent incorporates feedback mechanisms where robots communicate their current state, task completion status, and environmental observations to the auction system. This feedback loop enables the system to re-evaluate and re-distribute tasks based on actual system state and chronological dependencies, improving adaptability to uncertainties while maintaining efficient coordination.
2Reliability
If centralized control is used to coordinate robots, then chronological dependencies can be managed, but system complexity and computational burden increase
Solution Approach 1:
The patent segments the coordination problem by having each robot independently evaluate its own capability to perform tasks and bid accordingly. The centralized auction mechanism is segmented into distributed bid submissions from individual robots, reducing the computational burden on any single controller while maintaining the ability to satisfy chronological dependencies through the auction outcome.
Solution Approach 2:
The patent introduces an auction mechanism as an intermediary that mediates between individual robot capabilities and task requirements. This intermediary handles the complex coordination logic and chronological dependency satisfaction through structured bidding and allocation rules, simplifying the overall system architecture compared to direct centralized control of all robot interactions.
3Productivity
If tasks are pre-assigned to robots, then implementation efficiency is high, but adaptability to changing conditions and uncertainties is reduced
Solution Approach 1:
The patent implements periodic re-auctioning of tasks at defined intervals or trigger events, allowing the system to maintain efficient task execution while periodically re-evaluating allocations based on current conditions. This periodic action enables the system to adapt to changing conditions and uncertainties while preserving implementation efficiency during task execution phases.
Solution Approach 2:
The patent performs preliminary evaluation of robot capabilities and task requirements through the auction process before task assignment. This preliminary action allows the system to pre-compute optimal allocations based on current knowledge while maintaining the flexibility to re-auction if conditions change, thus preserving both efficiency and adaptability.
Data Source
AI summary
A method for operating a multi-agent system that includes multiple robots, each of the robots cyclically performing the following: starting from an instantaneous system state, ascertaining possible options, the options defining actions by which a transition may be achieved from an instantaneous system state to a subsequent system state; for each of the possible options, ascertaining action costs for performing an action specified by the option; performing an auction, the action costs values ascertained for each option being taken into consideration by each of the other robots; and performing an action, which corresponds to one of the options, as a function of all cost values ascertained or received for the relevant option, the action costs for a particular option each taking an experience parameter into consideration, which is a function of costs for past actions assigned to the particular option previously carried out by the multiple robots.