CS326A Motion Planning

Course Information Lecture Schedule Project Information Sites of Well-Known Researchers in Motion Planning Robotics Internet Resources Page

Course Information

Time/Place: TuTh 9:00--10:15, Gates 100
Instructor: Jean-Claude Latombe, Room 154, Gates, latombe@cs
Office Hours: After class, or drop by on ThFr in Room 154
Teaching Assistant: David Lin, Room 124, Gates, dlin@cs
Text: Robot Motion Planning, Jean-Claude Latombe, Kluwer, 1991. Not required.
Assignment: Programming project
Prerequisites: Programming in C. A good background in Math and Algorithms will help. Previous exposure to robotics concepts is also desirable.

Lectures (tentative schedule)

• 1. Introduction: What is motion planning. What are its applications.
  References:
  1. Robot Algorithms, by Dan Halperin, Lydia Kavraki, and Jean-Claude Latombe.
  2. Chapter 1 of Robot Motion Planning textbook.
  3. Chapter 13 of Computational Geometry - Algorithms and Applications , by M. de Berg, M. van Kreveld, M. Overmars, and O. Schwarzkopf.
  Class notes
• 2 and 3. Path planning for a rigid object in 2-D workspace:
  • Cell decomposition techniques
  • Roadmap techniques
  • Potential field techniques
  References:
  1. Chapter 13 of Computational Geometry - Algorithms and Applications, by M. de Berg, M. van Kreveld, M. Overmars, and O. Schwarzkopf.
  2. Chapters 4, 5, 6 and 7 of Robot Motion Planning textbook.
  Class notes
• 4 and 5. Path planning in high-dimensional configuration space:
  • Randomized path planning
  • Collision detection and distance computation
  References:
  1. Chapter 8 of Robot Motion Planning textbook.
  2. A paper describing algorithms, implementation issues, and experimental results for/with Probabilistic roadmaps.
  3. Two rather theoretical papers explaining why probabilistic roadmaps work well in practice:
     A Random Sampling Scheme for Path Planning.
     Capturing the Connectivity of High-Dimensional Geometric Spaces by Parallelizable Random Sampling Techniques.
  4. S. Quinlan. Efficient Distance Computation Between Non-Convex Objects. Proc. IEEE Int. Conf. on Robotics and Automation, San Diego, CA, 1994, pp. 117-123.
  5. B. Mirtich. V-Clip: A new collision detection algorithm for polyhedral objects. A public C++ implementation is available from: http://www.merl.com/projects/vclip/.
  Class notes
• 6 and 7. Path planning for nonholonomic robots
  • Concept of nonholonomy
  • Car-like and tractor-trailor robots
  • Lie brackets
  • Reeds and Shepp curves
  References:
  1. Chapter 9 of Robot Motion Planning textbook.
  2. J.P. Laumond, P. Jacobs, M. Taix, and R. Murray. A Motion Planner for a Nonholonomic Mobile Robot. IEEE Tr. Automatic Control, 10, 1994.
  3. J.C. Latombe. A Fast Path Planner for a Car-Like Indoor Mobile Robot. 9th Nat. Conf. on Artificial Intelligence, AAAI, Anaheim, CA, pp. 659-665, July 1991.
  Class notes
• 8. Configuration space concepts:
  • Parametrization
  • Topological and metric properties
  • Homotopy
  • Motion optimization
• 9. Trajectory planning with moving obstacles and multiple robots:
  • ConfigurationXTime space
  • Centralized vs. decoupled planning
  • Prioritized planning, velocity tuning
• 10 and 11. Assembly planning:
  • Generate-and-test
  • Contact analysis
  • Motion space and Nondirectional blocking graph
  • Interference diagram
  • Complexity of assembly planning
  References:
  • Geometric Reasoning About Mechanical Assembly.
  • Two-Handed Assemply Sequencing.
  • A General Framework for Assemply Planning: The Motion Space Approach.
  • An implementation of the motion space approach (Staat, Stanford).
  • Another implementation of the motion space approach (Archimedes system, Sandia Labs).
  • Assembly Planning Bibliography.
  • Directory of Assembly Planning Research.
  Class notes
• 12, 13 and 14. Motion planning with uncertainty:
  • Uncertainty in model
  • Online planning
  • Uncertainty in sensing and control
  • Notion of a preimage. Preimage backchaining
  • Landmark-based planning
  • Mobile robot navigation
  References:
  • Chapter 10 of Robot Motion Planning textbook.
  • Motion Planning with Uncertainty: A Landmark Approach.
  Class notes
• 15 and 16. Visibility-based motion planning:
  • Target finding (pursuit-evasion)
    Reference: A Visibility-Based Pursuit Evasion Problem.
  • Target tracking
    Reference: Motion Strategies for Maintaining Visibility with a Moving Target.
  • Model construction
  No class notes.
• 17. Manipulation planning:
  • Prehensile manipulation.
  • Nonprehensile manipulation.
• 18. Treatment planning in stereotaxic radiosurgery
• 19. Wrapup. Open problems. New applications.

Project Information

• Coordination of car-like robots
• Path planning for articulated robots
• Planning to find an unpredictable evader
• Assembly planning with translations
• Customized projects

Project tools

Some completed projects with JAVA animations (1997, 1998)

• Planning to Find an Unpredictable Evader, by Brian O'Connor
• Coordination of Multiple Car-like Robots, by Chirag Deepak Khopkar
• Planning to Find an Unpredictable Evader, by Aristides Gionis
• Nonholonomic Motion Planning for Multiple Car-like Robots in a Polygonal Environment, by Jian Bao
• Coordination of 3 Car-like Robots, by Lisa A. Laane
• Maintaining Visibility with Viewing Angle Constraints, by Mike Lin
• A Randomized Map Based Motion Planning Program for a Multi-Link Planar Manipulator, by Niels Smaby
• A Car-Like Robot Reacting to a Changing Environment in Real Time, by Pierre Louveaux
• Planning to Find An Unpredictable Evader, by Mike Slemmer
• Coordination of Multiple Car-Like Robots, by Chuck Han
• Probalistic Roadmap for a Floating Robot with a Serial Manipulator, by Andreas Huster
• Motion Planning for Track Robots in an Automated Manufacturing Cell, by Ju-Hsien Kao
• Planning to find an unpredictable evader, by Menelaos Karavelas
• Real-time path planning for a robot dynamic constraints in a field of moving obstacles, by Robert J. Kindel

Wheelchair Motion Animation, by Chuck Han (CS326B project, 1997).

Sites of Well-Known Researchers in Motion Planning

• Nancy Amato, Texas A&M University.
• Jean-Daniel Boissonnat, INRIA, Sophia-Antipolis, France.
• John Canny, U.C. Berkeley.
• Bruce Donald, Dartmouth College.
• Mike Erdmann, CMU.
• Maria Gini, University of Minnesota, Minneapolis.
• Ken Goldberg, U.C. Berkeley.
• Kamal Gupta , Simon Fraser University, Burnaby, BC, Canada.
• Dan Halperin, Tel Aviv University, Israel.
• Seth Hutchinson, University of Illinois, Urbana-Champaign.
• Lydia Kavraki, Rice University.
• Vijay Kumar, University of Pennsylvania. Philadelphia.
• Christian Laugier, INRIA, Grenoble, France.
• Jean-Paul Laumond, LAAS, Toulouse, France.
• Steve LaValle, Iowa State University.
• Tomas Lozano-Perez, MIT.
• Vladimir Lumelsky, University of Wisconsin - Madison.
• Matt Mason, CMU.
• Toshihiro Matsui ETL, Tsukuba, Japan.
• Mark Overmars , Utrecht University, The Netherland.
• Daniela Rus, Dartmouth College.
• Shankar Sastry, U.C. Berkeley.
• Achim Schweikard, Technische Universitat Munchen, Germany.
• Micha Sharir, Tel Aviv University, Israel.
• Anthony Stentz, CMU.
• Jeff Trinkle, Texas A&M University.
• Randy Wilson, Kodak Inc.

Robotics Internet Resources Page

Source of robotics books, movies, and toys