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Date:  Tue, April 17, 2018
Time:  9:30am
Location:  Holmes 287
Speaker:  Zhuoyuan Song, Faculty Candidate

Abstract:

Small autonomous robots as environmental perception instruments are often severely constrained in actuation capability, navigation system accuracy, and on-board processing capacity. The presence of ubiquitous geophysical flows tends to exacerbate challenges associated with the control and state estimation of these mobile platforms. Conventionally, background flows are considered as adversarial factors to the mobility and navigation accuracy of mobile robots. I advocate a new perspective on the role of background flows as ubiquitous navigation references and transportation "highways" for independent and networked autonomous robots. The first part of the talk introduces a novel flow-aided navigation method for long-term, mid-depth autonomous underwater vehicles (AUVs). This method leverages the dynamics of spatiotemporally varying background flows as navigation references in correcting the accumulative error of inertial navigation. The second part of this talk proposes a distributed, multi-robot flocking and flock guidance method by modeling robot swarms as continuous fluids. An implementation for nearly fuel-optimal guidance of large AUV groups in both artificial and real-world flow fields will be presented. Finally, I will discuss how these results have motivated future research directions including: 1) the design of the system middleware for consistent and secure collaboration between human supervisors and autonomous robot swarms; 2) long-term autonomy with concurrent flow-aided navigation and background flow dynamics learning. Besides these fundamental research topics, I will also briefly present several other related research projects including the development of compact AUVs, an underwater 4D visual guidance system for underwater obstacle avoidance, and the design of a low-cost underwater light communication system prototype.

Bio:

Zhuoyuan Song received his B.S. in Mechatronics Engineering and Automation from the Robot Institute at Shanghai University, Shanghai, China in 2011. He is currently a Ph.D. candidate in the Department of Mechanical and Aerospace Engineering at the University of Florida, where he received his M.S. in Mechanical Engineering in 2014. In 2018, he received the Attribute of a Gator Engineer Award from the Herbert Wertheim College of Engineering at UF. As a member of the Institute for Networked Autonomous Systems, he has conducted research funded by NSF, ONR, and AFRL. His research interests include several general aspects of robotics with emphases on state estimation and coordination of small aerial and underwater robots in extreme conditions involving strong background flows.