Abstract:

Motion planning is a well-studied and important problem in robotic automation. Interestingly, it has been addressed by the computational geometry and control systems community almost independently over the last two decades. In this talk I'll present an overview of the various approaches to robot motion planning as well as some of the popular ideas and techniques taken from both academic communities. In doing so, we'll uncover a remarkable mathematical connection between seemingly distinct approaches to the problem that helps to bring existing ideas together to solve important automation problems.

Bio:

Brian Paden received his B.S. and M.S. degrees in Mechanical Engineering in 2011 and 2013 respectively from UC Santa Barbara followed by a Ph.D. in Robotics from MIT in 2017. His research interests include optimal control, convex optimization, and software engineering in robotics applications. During his graduate studies he was affiliated with UCSB’s Center for Control, Dynamical Systems, and Computation and MIT’s Laboratory for Information and Decision Systems. He is presently a staff robotics engineer with Samsung’s Strategy and Innovation Center (SSIC). In 2018 he was awarded SSIC’s inventor of the year and was also a co-author on a paper awarded Best Paper at the IEEE International Conference on Automation Science and Engineering.