ABSTRACT:  The use of multi-robot systems is an effective way to efficiently accomplish tasks in many real-life applications and is crucial for large scale applications, such as environmental monitoring and water sampling in the ocean.

In this talk, I present my work on algorithmic solutions for environmental monitoring with multiple marine vehicles.

First, I show our efficient multirobot algorithms for a team of Autonomous Surface Vehicles governed by Dubins vehicle dynamics to cover of large areas of interest. Field trials with custom-modified Mokai ES-KAPE are presented, providing insights for improvements.

Second, I discuss the use of a heterogeneous team of robots to exploit their complementary capabilities to reduce the operational cost and increase the mission time for coral reef monitoring and water sampling. Using machine learning techniques to model the distribution of the observed phenomena, we developed adaptive exploration and sampling strategies that accounts for reduction in uncertainty. Experimental results from several field experiments together with some lessons learned will be presented.

The talk will conclude with a discussion on some of the open problems, such as reliable communication and recovery mechanisms, that still need to be fully addressed, together with some preliminary results.

BIO:  Alberto Quattrini Li is a research assistant professor at the Computer Science & Engineering Department of the University of South Carolina. He has been a postdoctoral fellow in the Autonomous Field Robotics Laboratory (AFRL), led by Professor Ioannis Rekleitis, in the same university. During 2014, he has been a visiting PhD student in the Robotic Sensor Networks Lab, directed by Professor Volkan Isler, at the Department of Computer Science and Engineering, University of Minnesota. He received a M.Sc. (2011) and a Ph.D. (2015) in Computer Science and Engineering from Politecnico di Milano, working with Professor Francesco Amigoni. His main research interests include autonomous mobile robotics and active perception, applied to the marine domain, dealing with problems that span from multirobot exploration and coverage to visual-based state estimation. He has worked with many ground and marine robots, including Autonomous Surface Vehicles and Autonomous Underwater Vehicles.