February 10, 2023 11:00am – 12:00pm
Location: TSRB auditorium
Ali Reza Pedram
Ph.D.
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. A smoothing algorithm will be presented to remove the possible sharp turns, which are common in sampling-based planners, in the output of the proposed algorithm. Finally, simulation results will be presented 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.
Biography
Ali Reza Pedram received the B.Sc. degrees in mechanical engineering and applied physics from the Sharif University of Technology, Tehran, Iran, in 2015, and the M.S. degree in mechanical engineering from the Sharif University of Technology in collaboration with the Max Planck Institute for Intelligent Systems, Stuttgart, Germany, in 2017. He is currently working toward the Ph.D. degree in mechanical engineering with the University of Texas at Austin, Austin, TX, USA. His research interests include motion planning, information theory, stochastic control, and optimization.
