Center for Computational Robotics
College of Engineering and Computing
University of South Carolina

The Center for Computational Robotics (CCR) at the University of South Carolina facilitates research, education, and outreach in robotics. Our mission is to solve complex scientific problems in perception, autonomy, and interaction for robots that operate in unstructured envirnoments. CCR projects span theoretical foundational and fielded applications. The center was established in 1983 as the Center for Machine Intelligence and assumed its current name, reflecting a renewed focus on robotics, in 2015.

CCR Seminar: History and Prospects for the Center for Computational Robotics
Speaker: Michael N. Huhns
Date: Friday, September 25, 11:30am
Location: SWGN 3A75

Abstract: The Center for Computational Robotics is the outgrowth of two previous research Centers, all with the goal of advancing the capabilities of physical and information systems. The activites of the previous centers can provide a context and perspectives that set the directions for the new Center. This seminar will describe these directions and outline the disciplines needed: from mechanics to ethics.

CCR Seminar: Robots at the end of their tether
Speaker: Dylan Shell, Texas A&M University
Date: Friday, October 16, 2:20pm
Location: SWGN Faculty Lounge

Abstract: There are lots of practical reasons why one might attach a tether to a mobile robot (providing power from off-board sources, high-speed communication to a base-station, etc.) but, since the tether constrains the motion of the robot, doing so makes the problem of moving the robot trickier than it would be otherwise. This talk will explore the motion planning problem for a planar robot connected via a cable to a fixed point in R^2. I'll describe how to visualize the configuration space manifold for such a robot, showing that it has regularity which can be used to produce a neat representation. This representation describes the manifold via (1) a discrete structure that characterizes the cable's position (2) an element within a single continuous cell. Further, when the tether has a constraint on its curvature, I'll show how Dubins’s theory of curves can be combined with work on planning with topological constraints to concisely represent the configuration space manifold, resulting in a data-structure that facilitates search for optimal paths.

Bio: Dylan Shell is a computer scientist with broad interests. He's an Associate Professor in the Department of Computer Science and Engineering at Texas A&M University, where he runs a laboratory focused on robotics and artificial intelligence. His research group aims to synthesize and analyze complex, intelligent behavior in distributed systems that exploit their physical embedding to interact with the physical world in a variety of ways. He has published papers on multi-robot task allocation, robotics for emergency scenarios, biologically inspired multiple robot systems, multi-robot routing, estimation of group-level swarm properties, minimalist manipulation, rigid-body simulation and contact models, human-robot interaction, and robotic theatre. His work has been funded by DARPA and the NSF; and he has been the recipient of the Montague Teaching award, the George Bekey Service award, and the NSF Career.

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