A Model for Multi-Agent Autonomy That Uses Opinion Dynamics and Multi-Objective Behavior Optimization

This paper reports a new hierarchical architecture for modeling autonomous multi-robot systems (MRSs): a nonlinear dynamical opinion process is used to model high-level group choice, and multi-objective behavior optimization is used to model individual decisions. Using previously reported theoretica...

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Bibliographic Details
Published in:	2024 IEEE International Conference on Robotics and Automation (ICRA) pp. 8305 - 8311
Main Authors:	Paine, Tyler M., Benjamin, Michael R.
Format:	Conference Proceeding
Language:	English
Published:	IEEE 13-05-2024
Subjects:	Costs Multi-robot systems Network topology Optimization
Online Access:	Get full text
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Summary:	This paper reports a new hierarchical architecture for modeling autonomous multi-robot systems (MRSs): a nonlinear dynamical opinion process is used to model high-level group choice, and multi-objective behavior optimization is used to model individual decisions. Using previously reported theoretical results, we show it is possible to design the behavior of the MRS by the selection of a relatively small set of parameters. The resulting behavior - both collective actions and individual actions - can be understood intuitively. The approach is entirely decentralized and the communication cost scales by the number of group options, not agents. We demonstrated the effectiveness of this approach using a hypothetical 'explore-exploit-migrate' scenario in a two hour field demonstration with eight unmanned surface vessels (USVs). The results from our preliminary field experiment show the collective behavior is robust even with time-varying network topology and agent dropouts.
DOI:	10.1109/ICRA57147.2024.10611032