Zhuoyuan SongUniversity of Florida, Gainesville
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 transportation “highways” and ubiquitous navigation references for independent and networked autonomous robots. The first part of this talk presents 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 (autonomous underwater vehicle) groups in both artificial and real-world flow fields will be discussed. The second part of the talk introduces a novel flow-aided navigation method for long-term, mid-depth AUVs. This method leverages the dynamics of spatiotemporally varying background flows as navigation references in correcting the accumulative error of inertial navigation. Finally, I will discuss how these results have motivated future research directions including: 1) the design of 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.
Zhuoyuan Song is currently a Ph.D. candidate and research assistant in the Department of Mechanical and Aerospace Engineering at the University of Florida (UF), where he received his M.S. degree in 2014. As a member of the Institute for Networked Autonomous Systems, he has conducted research funded by NSF, ONR, and AFRL under the advisement of Professor Kamran Mohseni. Before joining UF, he received the B.S. degree in Mechatronics Engineering and Automation from the Robot Institute at the Shanghai University, Shanghai, China in 2011. His research interests include several general aspects of robotics with emphases on coordination and localization of small aerial and underwater robots in extreme conditions involving strong background flows.
Host: Dr. Michael Scott
For more information, please contact Prof. Michael Scott, email@example.com.