CPSC 689: Special Topics in Randomized Motion Planning
Spring 2000
Course Information

Class Meeting: TBD (based on instructor & student availability)

Instructor: Nancy Amato
office: 414B Harvey R. Bright Bldg
office hours: TBD, or by appointment
email: amato@cs.tamu.edu
url: http://www.cs.tamu.edu/faculty/amato
office phone: 862-2275
home phone: 693-1855 (please don't call after midnight or before 9am)

Course homepage: http://www.cs.tamu.edu/faculty/amato/Courses/689

Prerequisite

A robotics course (CPSC 643 or CPSC 452) and an algorithms course (CPSC 629 or CPSC 311), or instructor's approval.

Reading Material

In this course, we will study papers from the current literature. The papers will be drawn from journals and conferences in the field such as IEEE Transactions on Robotics and Automation, International Journal of Robotics Research, IEEE International Conference on Robotics and Automation (ICRA), and the Workshop on the Algorithmic Foundations of Robotics. These will be made available electronically or at the copy center in HRBB.

There is no required text for the course. The following text covers useful background information and is recommended as a good reference:

Course Description

This course will cover topics in randomized motion planning. We define motion planning as: compute a sequence of motions that transforms a given (initial) arrangement of physical objects to another (goal) arrangement of those objects.

Many randomized motion planning methods have been developed in the realm of robotics research. For example, a typical problem might be to find a sequence of motions (called a path) to move a robot from one position to another without colliding with any objects in its workspace. However, the general motion planning problem we will study arises in many other application domains as well. For example, assembly planning (e.g., finding a valid order for adding the parts when building an engine), mechanical CAD studies (e.g., can you remove a certain part from an engine without taking the engine apart), virtual reality (e.g., finding appropriate fly-through paths in VR environments), medicine (e.g., performing insertability studies for artificial hip implants), and computational biology and chemistry (molecule docking in drug design, and protein folding).

The main goal of the course is to cover emerging paradigms in the field of motion planning, with a particular emphasis on randomized techniques. This will be done by reading and discussing recent articles from the literature (necessary since most of this work is not covered in current texts). Another goal of the course is to develop the necessary knowledge to understand how motion planning can be applied in various application domains. These include areas where motion planning has already made contributions (e.g., virtual prototyping, drug design), or might in the future (e.g., protein folding). This will occur `naturally' as many of the papers we'll read will describe their results in context of the applied problem.

The topics to be covered include the following (tentative):

Assignments

Paper Summaries. A short summary (1-2 pages) of each paper covered in the course will be due at the beginning of the class period in which that paper will be discussed. A schedule of the papers to be covered in the course is available in the reading schedule on the course webpage. We will generally cover 1-2 papers during each class period.

Paper Presentation(s). During the course of the semester, each student will be expected to present at least one paper to the class. This presentation will include (i) a summary of the results presented in the paper, (ii) an evaluation of the contribution of the paper, and (iii) a discussion of the relation of this paper to others read in the course and/or to other results in the literature (which may require reading other papers not on the assigned reading schedule).

Project

The goal of the project is to study in depth some issue related to motion planning. Projects may range from theoretical investigations of an open problem (solution not required for a good grade...), to implementation of a novel motion planning method (or some modification of an existing method), to experimental evaluations of known motion planning algorithms. Project topics will be selected by the student (in consultation with the instructor) by the end of the first month of the course. Projects may be done with partners (of course, more will be expected than if the project is done individually).

More details regarding the project are available in the project assignment on the course webpage.

Grading

Course grades will (tentatively) be determined as follows:

Computer Use and Accounts

You will need to be familiar with and have access to a web browser since many handouts for the course will be made available only on the web. In addition, email will be widely used for announcements regarding the course - you must read your email regularly.

All students registered for this course should have an account on the CS UNIX machines - if you do not already have an account you can sign up for one on the second floor of the Bright building.

Plagiarism

Plagiarism will not be tolerated in this course. As commonly defined, plagiarism consists of passing off as one's own the ideas, words, writings, etc., which belong to another. In accordance with this definition, you are committing plagiarism if you copy the work of another person and turn it in as your own, even if you should have the permission of that person. Plagiarism is one of the worst academic sins, for the plagiarist destroys the trust among colleagues without which research cannot be safely communicated.

If you have any questions regarding plagiarism, please consult the latest issue of the Texas A&M University Student Rules, under the section ``Scholastic Dishonesty.''

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