|
DEPARTMENT: MECHANICAL ENGINEERING
|
| COURSE #: EML 4840/5841,
3 credits |
COURSE TITLE: Introduction
to Mobile Robotics |
| TYPE COURSE: Dynamic Systems Elective |
TERM(S) OFFERED: Fall |
CATALOG DESCRIPTION:
Analytical dynamic modeling and dynamic simulation of mobile robots;
mobile robot sensors; basic methods of computer vision; Kalman filtering
and mobile robot localization; basic concepts of mapping; path planning
and obstacle avoidance; intelligent control architectures. |
PREREQUISITES:
Graduate standing or instructor's approval. |
AREA COORDINATOR: Dr. Emmanuel
Collins
RESPONSIBLE FACULTY: Dr. Emmanuel Collins
INSTRUCTOR OF RECORD:
Dr. Emmanuel Collins
Rm. B346, 410-6373, ecollins@eng.fsu.edu
ADDITIONAL INSTRUCTORS:
Dr. Pat Hollis (Modeling & Simulation), Rm. A232, 410-6319, hollis@eng.fsu.edu
Ms. Charmane Caldwell (Lab TA), Rm. B360, 410-6389, cvcaldwe@eng.fsu.edu
DATE OF PREPARATION: 8/26/06 EC
|
CLASS SCHEDULE:
MW 10:20 am - 11:35 am (***However, the 8 or so classes related to Modeling
& Simulation will be scheduled at a different time to accommodate
the teaching schedule of Dr. Hollis.)
LABORATORY SCHEDULE: The lab times will be announced throughout
the semester.
|
TEXTBOOKS/REQUIRED MATERIAL:
Textbook:
- Introduction to Autonomous Mobile Robots by Roland Siegwart and
Illah R. Nourbakhsh The MIT Press, 2004 (ISBN 0-262-19502-X)
- Supplementary: Notes from class web site
|
SCIENCE/DESIGN (%): 70% / 30%
CONTRIBUTION TO MEETING THE PROFESSIONAL COMPONENT:
70% Engineering science
30% Engineering design
|
COURSE TOPICS:
- Examples of mobile robots
- Applications of mobile robots
- Concept of autonomy
- Basic modeling concepts
- Vehicle kinematics
- Dynamic modeling of vehicles
- Sensor characteristics
- Description of mobile robot sensors
- Kalman filtering for sensor fusion and localization
- Global path planning algorithms
- Local path planning algorithms (obstacle avoidance)
- Control architectures
- Experimental implementation of mobile robot concepts
|
ASSESSMENT TOOLS:
- Homework
- Quizzes
- Laboratory reports
|
| COURSE OBJECTIVES*
|
(Numbers shown in brackets refer to department
educational outcomes - Please ask Dr. Shih to check these numbers)
- To provide an overview of the key concepts related to designing
and implementing mobile robots in practical applications. [1,3,4,5,8,9,10]
- To introduce the application of three dimensional, analytical modeling
and simulation to vehicles. [1,2,10,11]
- To provide an overview of the basic sensors used in mobile robots
and the ways that these sensors are characterized. [1,2,10,11]
- To introduce the concept of Kalman filtering and mobile robot localization.
[1,2,10,11]
- To introduce basic issues in computer vision for mobile robotics.
[1,2,10,11]
- To present standard path planning and obstacle avoidance algorithms.
[1,2,10,11]
|
| COURSE OUTCOMES* |
*(Numbers shown in brackets are links to course
objectives - check them out)
- Be able to describe a wide variety of autonomous vehicles and their
industrial or military applications. [1]
- Be able to describe the major physical subsystems associated with
mobile robots. [1]
- Be able to discuss the different levels of autonomy for mobile robots.
[1]
- Be able to use a software package such as ADAMS for simulating a
simple mobile robot. [2]
- Be able to develop analytical dynamic models for a simple mobile
robot. [2]
- Be able to describe the basic types of mobile robot sensors and
the principle of operation of a given sensor type. [3]
- Be able to discuss the way sensors are characterized and the precise
meaning of a given sensor characteristic. [3]
- Be able to design and simulate a Kalman filter for simple navigation
problems. [4]
- Be able to describe the basic issues in computer vision for mobile
robot applications. [5]
- Be able to describe the A* algorithm for path planning. [6]
- Be able to describe at least two alternatives to the A* algorithm
for path planning. [6]
- Be able to program a simple obstacle avoidance algorithm. [6]
|
