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

In a multirobot multitask scenario, redundancy is a fundamental characteristic, arising both from the larger number of agents relative to the tasks to be executed and from the surplus of input resources compared to the controlled variables required. In large-scale applications, scalability becomes critical for enhancing algorithm performance. This article addresses the decentralization of the dynamic control allocation policy in input-to-task redundant multiagent systems. We provide sufficient conditions for both the output and control matrices to assure that the information encoded in both signals depend only on data shared among neighbors. Additionally, we extend the task allocation paradigm to include input redundancy information as key factor in the allocation policy. The resulting framework enables simultaneous task and control allocation in a fully decentralized setting. The performance of the proposed method has been tested and validated by means of small-scale simulation on a heterogeneous multiagent system in a multitask scenario.