CS 498: Introduction to Planning Algorithms

CS 498: Introduction to Planning Algorithms

Spring 2010 Wed/Fri 11:00-12:15 Room 4405 Siebel Center (moved from 1131)
Registration: 50232 (3 hrs), 50234 (4 hrs) Instructor: Steve LaValle

Motivation: Planning algorithms are impacting technical disciplines and industries around the world, including robotics, computer-aided design, manufacturing, computer graphics, aerospace applications, drug design, and protein folding. This course is intended for computer scientists and engineers with interests in robotics, artificial intelligence, robotics, control theory, and the connections between them. The course focuses mainly on the modeling, algorithmic, and computational issues that arise when designing autonomous decision makers.

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Course Calendar:

Class # Date Lecture Topic Reading Assignments
Week 1
1 1/20 Course overview
Ch. 1

2 1/22 Discrete feasible planning; search algorithms
Sec. 2.1, 2.2

Week 2
3 1/27 Geometric representation; transformations
Sec. 3.1,3.2

4 1/29 transformations, topological concepts
Sec. 3.2,4.1

Week 3
5 2/3 topological concepts
Sec. 4.1.1 HW1 Assigned

6 2/5 manifolds
Sec. 4.1.2

Week 4
7 2/10 C-space, obstacles, motion planning
Sec. 4.2,4.3.1

8 2/12 C-space distance, sampling theory
Sec. 5.1.1,5.1.2,5.2.1 HW1 Due

Class #	Date	Lecture Topic	Reading	Assignments
Week 1
1	1/20	Course overview	Ch. 1
2	1/22	Discrete feasible planning; search algorithms	Sec. 2.1, 2.2
Week 2
3	1/27	Geometric representation; transformations	Sec. 3.1,3.2
4	1/29	transformations, topological concepts	Sec. 3.2,4.1
Week 3
5	2/3	topological concepts	Sec. 4.1.1	HW1 Assigned
6	2/5	manifolds	Sec. 4.1.2
Week 4
7	2/10	C-space, obstacles, motion planning	Sec. 4.2,4.3.1
8	2/12	C-space distance, sampling theory	Sec. 5.1.1,5.1.2,5.2.1	HW1 Due

Homework Assignments

HW1: Assigned: Feb. 3, Due: Feb. 12.
Solve book problems 2.2, 3.2, 3.8, 4.2, 4.8, and 4.10. Turn in written (or printed) copies of your work in class.

Topics
(estimated number of lectures given):

Introduction, motivation (1)
Chapter 1
Discrete planning (2)
Sections 2.1, 2.2
Continuous planning, sampling-based methods (8)
Sections 3.2, 4.1, 4.2, 4.3.1, 5.1-5.4, and possibly 7.1, 7.2, 7.6
Motion planning under differential constraints (6)
Sections 8.3, 13.1, 13.2, 14.1-14.3
Planning under uncertainty in predictability (7)
Sections 2.3, 9.2-9.4, 10.1-10.4
Sensors and information spaces (general concepts, sensor models, minimal information requirements, examples) (6)
Tutorial paper and parts of Chapters 11 and 12.

Textbook: Readings above are from Planning Algorithms, S. M. LaValle, Cambridge University Press, 2006. Also available for free download at http://planning.cs.uiuc.edu/.

Course Mechanics

Midterm (30%), Final Exam (40%), Homeworks (30%)
An independent project is required for those registered for four credit hours.
Homeworks will be a combination of written and programming assignments. There are no requirements to use a particular language for programming assignments. There will be three or four homework assignments during the semester.