Difference between revisions of "ME/CS 132b, Spring 2011"

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=== Collaboration Policy ===
 
=== Collaboration Policy ===
Students are encouraged to discuss and collaborate with others on the homework. However, you should write your own solution to show your own understanding of the material. You should not copy other people's solution or code as part of your solution. You are allowed to consult the instructors, the TAs, and/or other students. Outside reference materials can be used except for solutions from prior years or similar courses taught at other universities. Outside materials must be cited if used.
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Students are encouraged to discuss and collaborate with others on the homework. You are free to discuss general ideas about the problem. However, you should write your own solution to show your own understanding of the material. You cannot copy other people's solution as part of your solution. You cannot share code for homework or look at other people’s code. Reading aloud your code does not count as discussion. You are allowed to consult the instructors, the TAs, and/or other students. Outside reference materials can be used except for solutions from prior years or similar courses taught at other universities. Outside materials must be cited if used.
  
 
=== Course Texts ===
 
=== Course Texts ===

Revision as of 07:04, 29 March 2011

Advanced Robotics: Navigation and Vision

Instructors

  • Larry Matthies (coordinator), lhm@jpl.nasa.gov
  • Tom Howard, Yoshi Kuwata
  • Lectures: Tue/Thu, 2:30-4 pm, 306 TOM
  • Office hours: Tue/Thu, 4-5 pm, 303 TOM (by appointment only)

Teaching Assistants (me132-tas@caltech.edu)

Course Mailing List: me132-students@caltech.edu (sign up)

Announcements

There are currently no announcements.

Course Information

Prerequisites

There are no formal prerequisites for the course. ME 115 ab (Introduction to Kinematics and Robotics) is recommended but not necessary. Students are expected to have basic understanding of linear algebra, probability and statistics. We will review some of the required background materials during the first week of lectures. Besides these, students should have some prior programming experience and know at least one of the following languages: C, Python, or MATLAB. Depending on the background of the class, we will hold tutorials for some of the programming languages to help students get started.

Grading

There are no midterm/final exams for this course. The grade will be based on homework assignments and a course project. Late homework will not be accepted without a letter from the health center or the Dean. However, you are granted a grace period of m (m to be determined) late days throughout the entire term for weekly homework. Please email the TAs and indicate the number of late days you have used on the homework. No grace period is allowed for week-long labs.

  • Homework: Homework is usually due in one week after it is assigned. You can choose to
    • turn in a hard copy in class,
    • or, upload an electronic copy to the course server and send the TAs a note.
If you are unable attend the lecture, contact the TAs to find an alternative way to turn in your homework.
  • Course Project: Detail of this will be announced later in the course.

Homework Guidelines

  • Justify your answers. This will help us assign partial credits to your assignment even if the results are incorrect. On the other hand, we will deduct points if only results are shown without the necessary derivations.
  • You are encouraged to use professional libraries (such as OpenCV) for reading/writing files and analogous tasks. However, you cannot use functions which the homework implies you have to write yourself.
  • You will be given code examples in a few languages (Matlab, C++, Python), but you are free to use any language with which you are comfortable.
  • You are responsible for the parameters you choose. If we give you a “reasonable” value for a parameter that does not appear to work, you should try other values.

For electronic submissions (including your code):

  • Package code, data, and answers in a single .zip or .tgz file.
  • Upload the writeup as a single file to the course server. Do not upload multiple files for different parts of the writeup. The file must not be in proprietary formats (e.g. MS Word, Mathematica notebook). We recommend using PDF format to guarantee portability.
  • Separate code & commentary: do not write your discussion/derivation in the source files, but in a separate report file, clearly labeled as such.
  • Include instructions/scripts that allow reproducing your experiments with relatively little effort. For example, include a script “main.m” that calls the other files.

Collaboration Policy

Students are encouraged to discuss and collaborate with others on the homework. You are free to discuss general ideas about the problem. However, you should write your own solution to show your own understanding of the material. You cannot copy other people's solution as part of your solution. You cannot share code for homework or look at other people’s code. Reading aloud your code does not count as discussion. You are allowed to consult the instructors, the TAs, and/or other students. Outside reference materials can be used except for solutions from prior years or similar courses taught at other universities. Outside materials must be cited if used.

Course Texts

The required textbook is (also freely available online):

Lecture Notes

Week Date Topic Instructor
1 29 Mar (Tu) Kinematic and dynamics models Tom Howard
31 Mar (Th) Motion simulation Yoshi Kuwata
2 5 Apr (Tu) Search spaces Tom Howard
7 Apr (Th) Costing Tom Howard
3 12 Apr (Tu) Search algorithms Yoshi Kuwata
14 Apr (Th) Search algorithms (cont'd) Tom Howard
4 19 Apr (Tu) Navigtion and control Yoshi Kuwata
21 Apr (Th) Case studies Yoshi Kuwata
5 - 9 Course project

Homework

No homework assignment has been posted yet.

Older Announcements

There are no older announcements.