Multi-Robot Control via LTL Automata Decomposition
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Solution Overview
Problem
Existing methods for controlling multiple robots are inefficient in allocating tasks and planning actions, particularly for on-demand missions where task costs are unknown, as they require computing a combinatorial number of possible assignment costs, leading to complex planning problems.
Innovation Solution
The method uses Linear Temporal Logic (LTL) and Non-deterministic Finite Automata to automatically generate optimal action-level behavior for a team of robots by deriving a decomposition set of states and planning sequences of actions that minimize team costs, avoiding the need for complex combinatorial computations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If combinatorial computation of assignment costs is used for task allocation, then task allocation accuracy is improved, but computational complexity increases significantly
Solution Approach 1:
The patent segments the complex multi-robot task allocation problem into individual robot decision-making subproblems. Each robot independently evaluates tasks based on local information and predefined evaluation criteria, avoiding the need for combinatorial optimization across all robots. This segmentation transforms an NP-hard global optimization problem into multiple tractable local decision problems.
Solution Approach 2:
Each robot autonomously performs task evaluation and selection without requiring centralized coordination or iterative communication with other robots. The robots use self-service mechanisms to independently determine their next actions based on current system state and task priorities, eliminating the computational overhead of centralized combinatorial optimization.
2Measurement precision
If centralized coordination is used for multi-robot missions, then task allocation optimality is improved, but communication overhead and synchronization complexity increase
Solution Approach 1:
The patent divides the centralized control architecture into distributed autonomous decision-making units. Each robot segment operates independently with its own evaluation function, eliminating the need for continuous inter-robot communication and centralized coordination. This segmentation reduces communication overhead while maintaining allocation quality through localized optimization.
Solution Approach 2:
Robots autonomously evaluate and select tasks without requiring information exchange or synchronization with other robots. Each robot serves itself by independently computing task values based on current system state, eliminating communication overhead and synchronization complexity associated with centralized coordination.
3Measurement precision
If complex planning algorithms are used for unknown task costs, then mission completion quality is improved, but computational time increases
Solution Approach 1:
The patent pre-defines evaluation criteria and cost functions for task selection before missions begin. These preliminary evaluations establish decision rules that robots can quickly apply during execution without requiring complex real-time computations. This preliminary action prepares robots to make rapid decisions even when task costs are unknown, balancing quality and speed.
Data Source
AI summary
A method for controlling a plurality of agents to complete a mission, including deriving a decomposition set of decomposition states in a set of possible states of an automaton, wherein the automaton characterizes the mission, deriving a sequence of actions to be carried out by the plurality of agents depending on the decomposition set, where each action is to be carried out by at most one of the plurality of agents.