Due to technical difficulties, captions are unavailable but are forthcoming soon.

Abstract:
A very succesful approach for finding the motions for a robot to move from some initial configuration to a goal configuration is sampling-based motion planning. In this approach, the planner performs a systematic exploration, through sampling, of the configuraton space in order to build a connectivity map that the robot can follow. These planners trade completeness for probabilistic completeness, which means that given enough time, they will find existing paths with high probability, although they are not able to tell when there is no such path. In this talk, I will discuss how, while exploring the configuration space, sampling based planners are also able to extract features that can be useful to produce better maps. In the second part of my talk I will briefly discuss two applications for planning: multi-agent planning, and autonomous navigation.

Bio:
Marco Morales joined the Department of Computer Science at UIUC as a teaching associate professor in August 2020. He has been an associate professor at Instituto Tecnológico Autónomo de México (ITAM) in the Departments of Digital Systems and of Computer Science. He has also been a visiting professor at Texas A&M University and a lecturer at Universidad Nacional Autónoma de México (UNAM). He holds a Ph.D. in Computer Science from Texas A&M University, a M.S. in Electrical Engineering and a B.S. in Computer Engineering from Universidad Nacional Autónoma de México (UNAM). His main research interests are in motion planning and control for autonomous robots, artificial intelligence, machine learning and computational geometry.

Part of the Illinois Computer Science Speakers Series. Faculty Host: Nancy Amato