CS Colloquium: Konstantinos Karydis (University of California, Riverside) - Online mobile robot motion planning under uncertainty in unknown environments
Tue, Nov 30, 2021 @ 03:30 PM - 04:50 PM
Conferences, Lectures, & Seminars
Speaker: Konstantinos Karydis, University of California, Riverside
Talk Title: Online mobile robot motion planning under uncertainty in unknown environments
Series: Computer Science Colloquium
Abstract: Mobile robot motion planning under uncertainty is a challenging yet rewarding foundational robotics research problem with extensive applications across domains including intelligence, surveillance and reconnaissance (ISR), remote sensing, and precision agriculture. One important challenge is operation in unknown environments where planning decisions need to be made at run-time. In this talk we discuss recent results to address online motion planning in unknown environments. We consider two specific cases: 1) How to achieve resolution-complete field coverage considering the non-holonomic mobility constraints in commonly-used vehicles (e.g., wheeled robots) without prior information about the environment? 2) How to develop resilient, risk-aware and collision-inclusive planning algorithms to enable (collision-resilient) mobile robots to deliberately choose when to collide with locally-sensed obstacles to improve some motion planning metrics (e.g., total time to reach a goal).
To this end, we have proposed a hierarchical, hex-decomposition-based coverage planning algorithm for unknown, obstacle-cluttered environments. The proposed approach ensures resolution-complete coverage, can be tuned to achieve fast exploration, and plans smooth paths for Dubins vehicles to follow at constant velocity in real-time. Our approach can successfully trade-off between coverage and exploration speed, and can outperform existing online coverage algorithms in terms of total covered area or exploration speed according to how it is tuned. Further, we have introduced new sampling- and search-based online collision-inclusive motion planning algorithms for impact-resilient robots, that can explicitly handle the risk of colliding with the environment and can switch between collision avoidance and collision exploitation. Central to the planners' capabilities is a novel joint optimization function that evaluates the effect of possible collisions using a reflection model.
This way, the planner can make deliberate decisions to collide with the environment if such collision is expected to help the robot make progress toward its goal. To make the algorithm online, we present state expansion pruning techniques that can significantly reduce the search space while ensuring completeness.
This lecture satisfies requirements for CSCI 591: Research Colloquium.
Biography: Dr. Karydis is an Assistant Professor in the Department of Electrical and Computer Engineering at the University of California, Riverside (UCR). Before joining UCR, he worked as a Post-Doctoral Researcher in Robotics in GRASP Lab, which is part of the Department of Mechanical Engineering and Applied Mechanics at the University of Pennsylvania (Penn). His work was supported by Dr. Vijay Kumar, Professor and Nemirovsky Family Dean of Penn Engineering. He completed his doctoral studies in the Department of Mechanical Engineering at the University of Delaware, under the guidance of Prof. Herbert Tanner and Prof.
Host: Stefanos Nikolaidis
Audiences: Everyone Is Invited
Contact: Computer Science Department