Brett Lapin is a principal scientist at the Johns Hopkins University Applied Physics Laboratory and has more than 35 years’ experience in artificial intelligence applied to unmanned systems and intelligence. For the first 20 years, Lapin performed research and development in unmanned systems, designing and developing several unmanned ground vehicles for industry and the military and he was a member of the winning team in the first International Aerial Robotics Competition in 1992. For the past 10 years, Lapin has applied his expertise in machine intelligence and algorithms to the field of intelligence, leading an anomaly detection effort and developing discovery informatics algorithms. Lapin has worked on programs involving autonomous navigation, mobile robots, adaptive position estimation, computer vision, robotic arms, neural networks, reinforcement learning, data mining, sensor and data fusion, Kalman filters, vibration analysis, and fiber-optic gyroscopes, among others. In 1995, Lapin created and taught a graduate level neural networks class at Mercer University and a course in discovery informatics in JHU’s Division of Public Safety Leadership in 2009 and 2010. Currently, he teaches robotics in JHUs Engineering for Professionals program. Lapin has numerous publications and invited presentations and two patents.
This course introduces the fundamentals of robot design and development with an emphasis on autonomy. Robot design, navigation, obstacle avoidance, and artificial intelligence will be discussed. Topics covered in robot design include robot structure, kinematics and dynamics, the mathematics of robot control (multiple coordinate systems and transformations), and designing for autonomy. Navigation topics include path planning, position estimation, sensors (e.g., vision, ultrasonics, and lasers), and sensor fusion. Obstacle avoidance topics include obstacle characterization, object detection, sensors and sensor fusion. Topics to be discussed in artificial intelligence include learning, reasoning, and decision making. Students will deepen their understanding through several assignments and the term-long robot development project.
Robotics will provide fundamental knowledge of the core aspects of autonomous mobile robot design and development, including the current technological and algorithmic solutions that tackle the complexities of robot autonomy.
- By the end of the course, students should be able to:
Create autonomous robot design and performance goals, and develop the foundations of a robot design to achieve these goals
- Create basic sensor fusion, path planning, and position estimation algorithms to perform rudimentary robot perception, navigation, and obstacle avoidance
Specify a multisensor subsystem to enable desired autonomous performance
- Understand the basics of robot learning and decision making to enable higher levels of autonomy
When This Course is Typically Offered
Spring Term at the JHU Applied Physics Laboratory in Laurel, MD
- Robot Configuration
- Multiple Coordinate Systems and Transformations
- Robot Kinematics and Dynamics
- Navigation and Path Planning
- Sensors and Sensor Fusion
- Position Estimation
- Obstacle Avoidance
- Learning and Decision Making
Student Assessment Criteria
|Mid Term Exam||30%|
Term Project consists of using provided material to build a rudimentary mobile robotic platform that displays a certain level of autonomy (to be defined in class). All robots will be demonstrated during the final class.
Computer and Technical Requirements
Programming experience and proficiency in algorithm development are recommended.
Textbook information for this course is available online through the MBS Direct Virtual Bookstore.
There are no notes for this course.
(Last Modified: 02/11/2015 03:16:40 PM)