Research interests: planning algorithms, robotics, sensing, information spaces, computational geometry, control theory, artificial intelligence, growing sugar beets.

Fun Project: SToMP (Sensors, Topology, and Minimalist Planning)

Family pictures

This book presents a unified treatment of many different kinds of planning algorithms. The subject lies at the crossroads between robotics, control theory, artificial intelligence, algorithms, and computer graphics. The particular subjects covered include motion planning, discrete planning, planning under uncertainty, sensor-based planning, visibility, decision-theoretic planning, game theory, information spaces, reinforcement learning, nonlinear systems, trajectory planning, nonholonomic planning, and kinodynamic planning. The material grew from courses I taught at UIUC, Stanford, and Iowa State.

You are welcome to download and print out the whole manuscript for your own use.
See the Planning Algorithms page to download a FREE COPY.

Ph.D. Students
Leonardo Bobadilla Lars Erickson Kamilah Taylor

Benjamin Tovar Jingjin Yu

Other Information
External PhD Committees on Which I Have Served
A Message for Prospective Graduate Students
Previous Group Members
Funding Sources

Available Publications by Topic:

Sensing and Information Spaces	Visibility & Pursuit-Evasion	Mobile Robotics
Feedback Motion Planning	Planning w/ Differential Constraints	Optimal Control
Sampling-Based Motion Planning	RRTs	Coordinating Multiple Robots
Computional Biology	Computer Vision	Education
All Topics	Planning Algorithms book	BibTeX file

Abusing Roomba Robots
We are spending some time determining how to abuse Roomba robots into doing things for which they were not designed. For more information, see our Roomba Lab page.

Rapidly Exploring Random Trees (RRTs)
One of our most successful developments has been the RRT, which is designed for quickly searching high-dimensional spaces for feasible paths. See the RRT Page for more information.

Motion Strategy Library
This is an open-source general-purpose C++ library for implementing and comparing motion planning algorithms, for use in research, education, and industry. See the Motion Strategy Library page.
Recent News: A Windows XP binary distribution is now available on the MSL page, thanks to Wei Wang.

A Nearest-Neighbor Library for Motion Planning
A C++ library, written by Anna Yershova, that uses Kd-trees adapted to topological spaces that arise in motion planning. This enables fast nearest-neighbor computations in sampling-based motion planning algorithms.

Sampling the Space of 3D Rotations, SO(3)
A C++ library, written by Anna Yershova, that generates sequences of samples that are close to uniform and have regular neighborhood structure.

• Fall 2007: CS 598 Sensing, Actuation, and Computation
• Fall 2006: CS 498 Introduction to Planning Algorithms
• Fall 2005, Spring 2005, Fall 2003, Fall 2002: CS 273 Introduction to the Theory of Computation
• Spring and Fall 2004: Away on sabbatical
• Spring 2003: CS 497 Planning and Decision Making
• Spring 2002: CS 397 AI Planning and Decision Making
• Fall 2001: CS 497 Algorithmic Motion Strategies

• I am a co-organizer of the RSS Workshop, Topology and Minimalism in Robotics and Sensor Networks, June 2008.
• I am an co-chair of the IEEE RAS Technical Committee on Algorithms for Planning and Control of Robot Motion
• In 2002, I organized and hosted the 4th Midwest Mechanical Motion Meeting