A graph theoretic-based approach for deploying heterogeneous multi-agent systems with application in precision agriculture.

Mohammadreza Davoodi, University of Georgia

Abstract The main goal of this work is to address the problem of deploying a team of heterogeneous, autonomous robots in a partially known environment. To handle such arbitrary environments, we first represent them as a weighted directed graph. Then, two new partitioning algorithms are given that are capable of capturing the heterogeneity in robots dynamics. It is shown that the proposed partitioning methods assign a larger subgraph to a robot that has more resources or better capabilities compared to its neighbors. Next, a distributed deployment strategy is proposed to optimally distribute robots on the graph with the aim of monitoring specified regions of interest in the environment. Moreover, the application of the proposed methodology for monitoring an agricultural field is studied,  where a series of simulations and experimental studies are carried out to demonstrate that the proposed approach can yield an optimal partitioning and deployment and offer promise to be used in practice.

Biography Mohammadreza Davoodi received the M.Sc. and Ph.D. degrees in Electrical Engineering from Tarbiat Modares University, Tehran, Iran, in 2008 and 2012, respectively. Currently, he is a Post-Doctoral Fellow at University of Georgia (UGA), Athens, USA. Before joining UGA, he was a Post-Doctoral Fellow at Qatar University, Doha, Qatar and a Visiting Researcher at Concordia University, Montreal, QC, Canada. His research interests include fault diagnosis, robust control, multi-agent systems, networked unmanned systems, hybrid systems, agriculture robotics and convex optimization.