Abstract: In this talk, I will discuss new motion planning and control strategies to ensure safety under uncertainty in the environment, achieve collaborative multi-agent formation sensing for CADRE, and an algorithm for fault detection and identification that enables resilient networked robotic systems.
I will begin with a brief overview of the optimal motion planning problem and then discuss extensions of the standard motion planning problem to include uncertainty in dynamics and environment, enforce formation constraints to conduct formation sensing, and include information cost to perform a collaborative and resilient inspection. Next, I will briefly present a control extension using a receding horizon framework for tracking a nominal trajectory under uncertainty. Finally, I will discuss the algorithms for computing motion trajectories and show some preliminary results on Caltech's Spacecraft simulator testbed, mercury seven rovers, and empirical simulations.
About the Speaker: Yashwanth Kumar Nakka is currently a Postdoctoral Scholar at Jet Propulsion Laboratory. His research interests include design of autonomous system, spacecraft autonomy, motion planning and control under uncertainty, and nonlinear dynamics and control. He received the B. Tech. in aerospace engineering from the Indian Institute of Space Science and Technology, India, in 2011, the M. Sc. degree in aerospace engineering from the University of Illinois Urbana-Champaign, IL, USA, in 2016, and the M. Sc. degree and a Ph.D. in space engineering from California Institute of Technology, CA, USA, in 2017, and 2021, respectively. He was an engineer for the GSAT-15 and 16 missions at the Indian Space Research Organization during 2011-2014.
He received the best student paper award at the 2021 American Institute of Aeronautics and Astronautics Guidance, Navigation, and Controls conference and the best paper award at the 11th International Workshop on Satellite Constellations and Formation Flying. In addition, he serves as AE for AIAA Scitech 2023 for Autonomy and AI track for Aerospace Vehicle GNC.
WebEx: https://jpl.webex.com/jpl/j.php?MTID=m5f5578ffc8daa4da9fd02df4bc4792f1
Meeting number (access code): 2763 779 6217
Meeting password: K26DwUgJbG2
