A Decentralized Control and Task Allocation Framework for Heterogeneous Redundant Multiagent Systems

Area of Science:

• Robotics
• Control Theory
• Artificial Intelligence

Background:

• Multirobot multitask systems inherently possess redundancy in agents, tasks, and resources.
• Scalability is crucial for improving algorithm performance in large-scale applications.
• Decentralized control is essential for efficient operation of multiagent systems.

Purpose of the Study:

• To decentralize the dynamic control allocation policy in input-to-task redundant multiagent systems.
• To ensure information in control signals depends only on shared neighbor data.
• To integrate input redundancy into the task allocation policy for simultaneous allocation.

Main Methods:

• Developing sufficient conditions for output and control matrices.
• Extending the task allocation paradigm to incorporate input redundancy.
• Implementing a fully decentralized framework for simultaneous task and control allocation.

Main Results:

• Information dependency is limited to neighbor-shared data.
• Simultaneous task and control allocation is achieved in a decentralized manner.
• The proposed method demonstrates effective performance in simulations.

Conclusions:

• The proposed decentralized framework effectively manages redundant resources in multiagent systems.
• The method enhances scalability and performance for large-scale applications.
• Validated through simulations, the approach offers a robust solution for complex multitask scenarios.