Towards Resilient Low-Cost Robot Teams for Autonomous Aquatic Exploration
Why is the aquatic world still mostly unexplored despite the great progress in autonomous robotics? This talk delve into some of the main challenges that limit robots in such a task and solutions we designed towards resilient low-cost aquatic robot teams. First, I will talk about algorithms for color correction and fusion of low-cost camera and echosounder that improved the situational awareness of underwater robots. Second, I will present exploration strategies that explicitly consider uncertainties and constraints allowing robots to effectively operate in the real world. Then, I will touch on enabling low-cost manipulation through hardware/software co-design for underwater construction. Each part will include discussion of field experiments and lessons learned. The talk will conclude with a discussion on some of the open problems and current work to achieve the long-term goal of a ubiquitous collaborative multi-agent/multi-robot system that can support large scale aquatic applications, such as environmental monitoring or archaeological exploration.
Alberto Quattrini Li is an assistant professor in the Department of Computer Science at Dartmouth College and co-director of the Dartmouth Reality and Robotics Lab. His main research (currently funded by the National Science Foundation) covers autonomous mobile robotics and active perception, applied to the aquatic domain, dealing with problems that span from multirobot exploration and coverage to multisensor fusion based state estimation. He has worked with many ground and marine robots, including Autonomous Surface Vehicles and Autonomous Underwater Vehicles. He was a postdoctoral fellow and research assistant professor in the Autonomous Field Robotics Laboratory (AFRL), led by Professor Ioannis Rekleitis, in University of South Carolina from 2015 to 2018. During 2014, he was 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.