Information-Theoretic Approach to Gaussian Belief Space Path Planning for Minimum Sensing Navigation
ECE 595 Department Seminar Series
October 1, 2021
11:00 AM - 12:15 PM
Location
Lecture Center D1
Address
804 South Halsted Street, Chicago, IL 60607
Calendar
Download iCal FileInformation-Theoretic Approach to Gaussian Belief Space Path Planning for Minimum Sensing Navigation
Presenter: Takashi Tanaka, University of Texas at Austin
Abstract: Motion planning and strategic sensing are inseparable problems for autonomous robots navigating in uncertain environments under perceptual resource constraints. In this talk, a new path planning methodology for a mobile robot in an obstacle-filled environment to generate a reference path that is traceable with moderate sensing efforts will be discussed. In this framework, the desired reference path is characterized as the shortest path in an obstacle-filled Gaussian belief manifold equipped with a certain information-geometric distance function. The distance function introduced can be interpreted as the minimum information gain required to steer the Gaussian belief. An RRT*-based numerical solution algorithm is presented to solve the formulated shortest-path problem. The asymptotic optimality of the proposed path planning algorithm will also be discussed. Finally, simulation results demonstrating that the proposed method is effective in various robot navigation scenarios to reduce sensing costs, such as the required frequency of sensor measurements and the number of sensors that must be operated simultaneously, will be presented.
Speaker Bio: Takashi Tanaka is an assistant professor in the department of aerospace engineering and engineering mechanics at the University of Texas at Austin since 2017. He received his B.S. degree from the University of Tokyo in 2006, and M.S. and Ph.D. degrees from UIUC in 2009 and 2012, all in aerospace engineering. Prior to joining UT Austin, he held postdoctoral researcher positions at MIT and KTH Royal Institute of Technology. His research interest is broad in control, optimization, games and information theory; most recently their applications to networked control systems, real-time data sharing and strategic perception. He is the recipient of the DARPA Young Faculty Award, the AFOSR Young Investigator Program award, and the NSF Career award.
Faculty Host:Shuo Han (hanshuo@uic.edu)
Date posted
Sep 27, 2021
Date updated
Oct 1, 2021