Randomized Motion Planning: Techniques and Applications
University of Padova
Course Information
Fall 2004

Instructor: Nancy Amato
email: amato@cs.tamu.edu
url: http://parasol.tamu.edu/~amato
off: Room 405, DEI/G
tel: +39 049 827 7942

Class Meeting:
Nov 12, 14:30-16:30, Sala Riunioni DEI/G
Nov 16 & 18, 11:00-13:00, Sala Riunioni 201 DEI/A
Nov 24, 11:00-13:00, Sala Riunioni 301 DEI/A
Nov 25, 12:00-14:00, Sala Riunioni 201 DEI/A
Nov 30, 11:00-13:00, Sala Riunioni 201 DEI/A

Course homepage: http://parasol.tamu.edu/~amato/Courses/padova04/

Prerequisite:
An algorithms course or instructor's approval. Note: No prior experience with robotics is assumed.

Reading Material:
Conference and journal papers from the recent literature. These will be available electronically.

Course Description:
The main objective of the course will be to acquaint students with the state-of-the-art in randomized motion planning, and in particular with the class of techniques known as probabilistic roadmap methods. The course will cover basic techniques and applications.

The course will be 6 sessions.

Topics and Schedule (Tentative):

• Motion Planning Techniques (3 sessions)
  • Motion Planning Basics
  • Probabilistic Roadmap Methods (PRMs)
    • The original PRM method (uniform sampling)
    • Biased-Sampling Methods (OBPRM, MAPRM, Gaussian PRM, Bridge Test, etc)
    • Methods for Highly Constrained Problems (closed chain systems)
  • PRM Optimization Techniques
    • Lazy Evaluation Methods (Lazy PRM, Fuzzy PRM, C-PRM),
    • Iterative Solution Methods (IRC)
    • Cooperative User/Planner Systems (Haptics)
  • Tree-Based Methods
    • Ariadnes Clew Algorithm, RRT

• Applications in Animation and Graphics (1.5 sessions)
  • Deformable Objects
  • Group Behaviors (multiple moving objects)

• Applications in Computational Biology (1.5 sessions)
  • Protein/Ligand Binding
  • Protein Folding
  • RNA Folding

Grading:
Course grades will be based on class participation and a project.

Project:
The goal of the project will be to study in depth some issue related to randomized motion planning. Projects may range from a detailed report of a current paper(s), to an investigation of an open problem in randomized motion planning (solution not required for a good grade...), to an experimental studies of known algorithms. Project topics should be selected by the student in consultation with the instructor. The project should be completed within 3 weeks of the end of the course.