Multi-Robot Auction Coordination Under Uncertainty and Temporal Constraints
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Solution Overview
Problem
Efficient planning algorithms for coordinating robots in multi-agent systems under uncertainties and temporal dependencies in task specifications are currently lacking, particularly in nondeterministic environments.
Innovation Solution
A method involving a deterministic finite automaton and a decentralized auction process is used to distribute tasks among robots, where each robot cyclically assesses options for state transitions, calculates and shares action costs, and executes the option with the lowest costs, considering estimated mission costs and probabilistic models to adapt to changing conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a conventional task distribution method is used without considering uncertainties and temporal dependencies, then the coordination process is simpler, but the activity instructions for individual robots are not satisfactorily associated and task execution reliability deteriorates
Solution Approach 1:
The patent segments the task specification into subtasks and further into atomic tasks, creating a hierarchical decomposition that allows reliable task execution while managing complexity through structured breakdown. The LTL specification is divided into state paths, transitions, and individual atomic tasks that can be independently evaluated and assigned.
Solution Approach 2:
The patent introduces an intermediary auction process that acts as a mediator between the task specification and robot assignment. This decentralized auction mechanism coordinates robots by having them bid for atomic tasks based on their current states and capabilities, resolving the contradiction between reliable task execution and coordination complexity.
2Productivity
If a decentralized auction process with comprehensive cost calculation is used, then task distribution efficiency is improved, but the computational complexity and time required for each auction round increases
Solution Approach 1:
The patent applies preliminary action by pre-calculating and storing cost functions for different task execution scenarios before the auction process begins. Robots pre-assess their capabilities and costs for various atomic tasks, so that during the actual auction rounds, they can quickly submit bids without performing complex real-time calculations, thus improving efficiency while reducing execution time.
Solution Approach 2:
The patent implements dynamics by making the auction process adaptive and iterative. The system dynamically adjusts the auction rounds based on task completion status and robot state changes, allowing the coordination to evolve over time rather than requiring a single comprehensive auction, thereby balancing efficiency and computational burden.
3Adaptability or versatility
If atomic tasks are reassigned after each state change, then adaptability to changing conditions is improved, but the frequency of auction rounds increases leading to higher computational overhead
Solution Approach 1:
The patent implements feedback mechanisms where robots report their state changes and task completion status to the coordination system. The system uses this feedback to determine when re-auction is necessary, rather than forcing auctions after every state change. This selective re-auction approach maintains adaptability to changing conditions while reducing unnecessary computational overhead from frequent auction rounds.
4Measurement precision
If comprehensive cost functions including empirical costs are used, then the quality of task-robot matching is improved, but the data processing and cost calculation complexity increases
Solution Approach 1:
The patent applies preliminary action by pre-computing cost functions and storing them in lookup tables or databases before the auction process. Empirical costs and other parameters are calculated in advance based on robot capabilities and task requirements, so that during auction rounds, the system only needs to retrieve and compare pre-computed values rather than performing complex calculations in real-time, thus improving matching quality while managing computational complexity.
Data Source
AI summary
A method for operating a multi-agent system including multiple robots. Each robot cyclically carries out the following: starting from an instantaneous system state, ascertaining possible options, the options defining actions via which a transition from an instantaneous system state to a subsequent system state may be achieved; for each possible option, ascertaining action costs for carrying out an action indicated by the option; carrying out an auction, the action cost values ascertained for each option being taken into account by each of the other robots; and executing an action that corresponds to one of the options as a function of all cost values ascertained or received for the option in question, the action costs for an option taking into account an empirical parameter that is a function of costs for past actions, which have already been carried out and which are associated with the option, of the multiple robots.