Jeffrey C. Trinkle
Professor and Chairman
Department of Computer Science
Ph.D Systems Engineering, University of Pennsylvania, Aug. 1987.
B.S. Engineering Science and Mechanics, Georgia Tech Sept. 1979
B.S. Physics, Ursinus College, May 1979
Professor and Chair, Dept. of Computer Science, Rensselaer Polytechnic Institute, 2003-present
Principal Member of Technical Staff, Sandia National Laboratories, 1998-2003
Associate Professor, Dept. of Computer Science, Texas A&M University, 1995-2000
Assistant Professor, Dept. of Computer Science, Texas A&M University, 1990-1995
Assistant Professor, Dept. of Systems and Industrial Engineering, University of Arizona, 1988-1990
Research Assistant, Dept. of Systems Engineering, University of Pennsylvania, 1982-1987
Mechanical Engineer, Lawrence Livermore Laboratory, 1979-1982
Associate Professor, Dept. of Electrical Engineering, Rice University, 1996-1997
Assistant Professor, Dept. of Mechanical Engineering, University of Wollongong, 1987-1988
Summer Faculty Fellow, Jet Propulsion Laboratory, summer 1989
Designer/Draftsman, Gupton Engineering Associates, 1978
Jeffrey C. Trinkle received his bachelor's degrees in Physics (1979) and Engineering Science and Mechanics (1979) from Ursinus College and Georgia Institute of Technology, respectively. In 1987, he received his Ph.D. from the Department of Systems Engineering at the University of Pennsylvania. Since 1987, he has held faculty positions at the Department of Systems and Industrial Engineering at the University of Arizona and the Department of Computer Science at Texas A&M University. From 1998 to 2003 he was a research scientist at Sandia National Laboratories in Albuquerque, New Mexico. He is now Professor and Chair of Computer Science at Rensselaer Polytechnic Institute in Troy, New York. Prof. Trinkle's primary research interests are in the areas of robotic manipulation, multibody dynamics, and automated manufacturing.
I am generally interested in developing intelligent systems that can move around in and manipulate their environments. I try to understand such systems through a combination of techniques from computer science, mathematics, and engineering. In order to plan a task, one must first be able to reliably predict the outcomes of the application of possible actions. For example, if a robot pushes against a box on the floor, we should be able to predict if the box will stick, slide, or tumble. I have helped develop new multibody dynamics algorithms to answer this sort of question. Once we can make such predictions, we can then think about designing a series of actions to accomplish a specific goal such as, "Put new staples in the stapler." One common approach to this type of problem is to decompose the possible states of the stapler into equivalence classes, with each class represented by a node in a graph. The nodes can then be connected with directed arcs determined by predicting the outcome of the application of basic manipulation actions (such as "push left") to the states. Once the arcs are determined, the planning problem can be solved by searching the graph for a path connecting the nodes containing the starting and goal states of the stapler.
The following two currently active projects focus on problems fundamental to the goal of autonomous manipulation planning described above:
The DaVinci Project
In the DaVinci project, my colleagues and I are studying a new class of problems, differential algebraic inequalities, which are useful in modeling dynamic systems with unilateral constraints such as robotic systems that contact their environments (view some results obtained with rigid body dynamics). This project is currently supported by the National Science Foundation (DMS/FRG, CISE/RCV, and MRI) and RPI.
Exact Motion Planning
With professor Jim Milgram (Stanford) and Dr. Guanfeng Liu (RPI), we are exploring the use of techniques of pure mathematics applied to the problem of planning collision-free motions of linkages such as robots and protein molecules. Methods of geometry, topology, and homology are being used to determine solution existence and sub-optimal paths when they exist. Some results applicable to closed kinematic chains are available here. This project is currently supported by RPI.
Here are some problems I've worked on and/or have interest in working on:
- Dexterous Manipulation Planning
- Real-time and Parallel Algorithms for Rigid Body Dynamics (enabling a new type of computer games and improved analysis of mechanical systems)
- Automated Assembly Planning
- Nano-Tribology (Do we have to predict part motions differently when they are tiny?)
- Fixture Design
- Robot Grasp Synthesis
Rensselaer Polytechnic Institute
110 8th Street
Troy, N.Y. 12180 USA
Laboratory: MRC 331, 332
Phone: (518) 276-8291
Fax: (518) 276-4033
trinkN0SPAM at cs dot rpi dot edu