Chinwe will discuss advances done on the topological representation of the planning spaces for robots & the topological tools she developed.
Chinwe Ekenna, Assistant Professor, University at Albany, State University of New York
Title: Improving Motion Planning Algorithms - Pre and Post Processing Phases.
Abstract: Motion planning methods have advanced tremendously to handle high dimensional and complex spaces. However, there is still a challenge in understanding the approximations being done to sample the environment, and how much sampling is needed to ensure that a path is formed if it exists. My talk will discuss advances done on the topological representation of the planning spaces for robots and the topological tools that I developed to help explore, measure and provide an upper-bound to the number of samples needed in a given robot environment. In the same vein, I’ll be discussing new methodologies that provide collision avoidance strategies during trajectory planning in the robot’s state space utilizing the topology information derived.
Bio: Chinwe Ekenna is an Assistant Professor in the Department of Computer Science at the University at Albany, State University of New York, and the Director of the Robotics, Algorithm and Computable Systems (RACS) Laboratory. Chinwe is a faculty advisor for UAlbany's student chapter of the Association for Computing Machinery-Women (ACM-W) and participates in publicly engaged activities which encourage young women in STEM. Chinwe's research centers on intelligent motion planning applied to robotics and proteins. She has explored intelligent adaptation of robotic motion planning to improve planning time and topological data analysis methods to capture important features of robot planning spaces. Her research interest includes Machine learning, computational geometry, and computational biology.
**Contact Susan.Perry.Cable@dartmouth.edu for Zoom link.**
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