Density-aware decentralised multi-agent exploration with energy constraint based on optimal transport theory

Abstract

This paper addresses a density-aware multi-agent exploration problem based on an Optimal Transport (OT) theory while considering energy constraints of a multi-agent system. The density-aware exploration means how a team of agents (robots) cover a given domain, reflecting a priority of areas of interest represented by a density distribution, rather than simply following a preset of uniform patterns. To achieve the density-aware multi-agent exploration, the optimal transport theory that quantifies a distance between two density distributions is employed as a tool, which also serves as a means of similarity measure. Energy constraints for a multi-agent system are then incorporated into the OT-based density-aware multi-agent exploration scheme. The proposed method is developed targeting a decentralised control to cope with more realistic scenarios such as communication range limits between agents. To measure the exploration efficiency, the upper bound of the similarity measure is proposed, which is computationally tractable. The developed multi-agent exploration scheme is applicable to a time-varying distribution as well, where a spatio-temporal evolution of the given reference distribution is desired. To validate the proposed method, multiple simulation results are provided.

Keywords:

• Multi-agent exploration
• optimal transport
• efficient robot exploration
• density-aware exploration

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported by New Mexico NASA EPSCoR Research Infrastructure Development (RID) Program with Cooperative Agreement [80NSSC19M0181].

Notes on contributors

Kooktae Lee

Kooktae Lee received his B.S. and M.S. degrees in the Department of Mechanical Engineering from Korea University in 2006 and 2008, respectively. He received his Ph.D. degree in the Department of Aerospace Engineering from Texas A&M University in 2015 and continued his research as a postdoctoral research associate from 2015 to 2016. He was a postdoctoral scholar in the Department of Mechanical & Aerospace Engineering at the University of California San Diego from 2016 to 2017. Since 2017, he is an assistant professor with the Department of Mechanical Engineering at New Mexico Institute of Mining and Technology. His major research interests include robotics and control, multi-agent systems, distributed networked control systems, uncertainty quantification, and asynchronous algorithms.