Multi-Agent Zone Allocation Using Diffeomorphic State Mapping
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Solution Overview
Problem
Existing multi-agent systems face challenges in allocating and preserving zones for agents, as conventional distributed control protocols assume unconstrained state spaces, leading to inefficiencies and unreliability in applications like surveillance, reconnaissance, and traffic management.
Innovation Solution
A method involving a monotonically increasing aligned diffcomorphic (mia-diffcomorphic) map transforms agent states into unconstrained representations, allowing for the generation and execution of control signals that maintain agents within designated zones using a distributed adaptive control protocol.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional distributed control protocols are used, then agents can operate autonomously in multi-agent systems, but agents cannot be reliably constrained to remain within designated zones
Solution Approach 1:
The patent transforms the constrained state space parameters into an unconstrained representation using a monotonically increasing aligned diffeomorphic map. This parameter transformation allows conventional control protocols to operate on transformed coordinates while the actual agent states remain constrained within designated zones, resolving the contradiction between reliability of zone preservation and adaptability to different control scenarios.
Solution Approach 2:
The patent introduces a state transformation map as an intermediary between the constrained physical state space and the unconstrained control space. This intermediary transformation layer enables conventional distributed control protocols to function effectively while indirectly ensuring zone constraints are maintained, thus improving reliability without limiting adaptability.
2Reliability
If zone constraints are imposed on agents, then agents remain within designated zones, but control complexity increases due to state transformation requirements
Solution Approach 1:
The patent applies parameter transformation through a monotonically increasing aligned diffeomorphic map that converts constrained state variables into unconstrained coordinates. This transformation simplifies the control problem by allowing standard control protocols to operate in the transformed space, while the transformation itself handles the complexity of zone constraints, thereby improving reliability without significantly increasing overall control complexity.
3Ease of operation
If distributed control protocols assume unconstrained state spaces, then control algorithms are simpler to implement, but they fail to ensure agents remain within assigned zones
Solution Approach 1:
The patent introduces a state transformation map as an intermediary that bridges the unconstrained control algorithm space and the constrained physical zone space. This allows simple unconstrained control algorithms to be implemented in the transformed coordinate system while the transformation ensures zone constraints are automatically satisfied in the physical space, thus maintaining ease of operation while improving reliability.
Solution Approach 2:
The patent transforms the state space parameters using a monotonically increasing aligned diffeomorphic map, allowing conventional unconstrained control algorithms to operate on transformed variables. The transformation inherently handles zone constraints, enabling simple algorithm implementation while ensuring reliable zone constraint satisfaction through the parameter transformation mechanism.
Data Source
AI summary
Methods and systems for generating control systems that can recognize and preserve zone allocation constraints in a multiagent system are disclosed. The methods and systems provide for determining a given agent's state within its current zone allocation, transforming the given agent's state into an unconstrained representation (e.g., using a mia-diffeomorphic mapping), determining a virtual control signal for the given agent based on the unconstrained representation, transforming the virtual control signal into an actual control signal via an inverse function, and communicating the actual control signal to the given agent based on a role of the agent and a communication protocol of the system. Other aspects, embodiments, and features are also claimed and described.