Dr. Xiaobo Tan is an MSU Foundation Professor and the Richard M. Hong Endowed Chair in Electrical and Computer Engineering at Michigan State University (MSU). He received his bachelor’s and master’s degrees in automatic control from Tsinghua University, Beijing, China, in 1995, 1998, respectively, and his Ph.D. in electrical and computer engineering (ECE) from the University of Maryland in 2002. His research interests include bio-inspired robots, soft sensors and actuators, and modeling and control of systems with hysteresis. In particular, his group has developed and field-tested autonomous underwater and surface robots for mobile sensing applications. He has published over 300 papers and been awarded four US patents in these areas.
Dr. Tan is a Fellow of IEEE and ASME. He is a recipient of the NSF CAREER Award (2006), MSU Teacher-Scholar Award (2010), MSU College of Engineering Withrow Distinguished Scholar Award (2018), Distinguished Alumni Award from the ECE Department at University of Maryland (2018), and multiple best paper awards. He is currently a Senior Editor for IEEE/ASME Transactions on Mechatronics and serves on the Executive Committee of the ASME Dynamic Systems and Control Division. Dr. Tan is keen to integrate his research with educational and outreach activities, and has served as Director of an NSF-funded Research Experiences for Teachers (RET) Site program at MSU from 2009 – 2016 and Curator of a robotic fish exhibit at MSU Museum in 2016-2017.
Fish and Robot: Bio-inspiration, Underwater Sensing, and the Role of Dynamics and Control
Evolution over the course of 500 million years has endowed fish with superior swimming and sensory capabilities in water. This has not only captivated the interest of biologists, but also spurred the development of underwater machines aiming to emulate fish’s locomotion and sensing marvels. In this talk I will first discuss efforts in developing hydrodynamic sensing systems inspired by lateral lines, the flow-sensing organ of fish. I will then illustrate the important role played by advanced modeling and control tools in optimizing robotic fish’s locomotion performance. I will further introduce gliding robotic fish, a new class of robotic fish that incorporates gliding to boost locomotion energy-efficiency, and discuss its application to autonomous underwater sensing. In one example, the unique spiral dynamics of gliding robotic fish is exploited in sampling the distribution of harmful algae along water columns. In another example, a network of gliding robotic fish is proposed for tracking the movement of invasive fish species with acoustic telemetry, and we show how distributed filtering algorithms can be used to localize the moving target. Both examples will be supported with results from field experiments.
