The field of robotics integrates sensing, information processing, and movement to accomplish specific tasks in the physical world. As such, it encompasses several topics, including mechanics and dynamics, kinematics, sensing, signal processing, control systems, planning, and artificial intelligence. Applications of these concepts appear in many areas including medicine, manufacturing, space exploration, disaster recovery, ordinance disposal, deep-sea navigation, home care, and home automation.
The faculty of the Laboratory for Computational Sensing and Robotics (LCSR), in collaboration with the academic departments and centers of the Whiting School of Engineering, offers a robotics minor in order to provide a structure in which undergraduate students at Johns Hopkins University can advance their knowledge in robotics while receiving recognition on their transcript for this pursuit. The minor is not “owned” by any one department, but rather it is managed by the LCSR itself. Any student from any department within the university can work toward the minor.
Robotics is fundamentally integrative and multidisciplinary. Therefore, any candidate for the robotics minor must develop a set of core skills that cut across these disciplines, as well as obtain advanced supplementary skills.
Please visit https://lcsr.jhu.edu/robotics-minor/ for current course listings and full minor policies.
Core Skills Include the Following
- Robot kinematics and dynamics (R)
- Systems theory, signal processing and control (S)
- Computation and sensing (C)
Supplementary advanced skills may be obtained in specialized applications, such as space, medicine, or marine systems; or in one of the three core areas listed above.
The full minor course listing, provided below and available at https://lcsr.jhu.edu/robotics-minor/, specifies which courses fulfill these requirements. Please always check the website for the most up-to-date listing of courses. Note that all core areas must be covered, but that any advanced/supplementary courses can be chosen from the list. This allows students to strike a balance between breadth and depth.
- All students interested in the minor are required to make an appointment with Alison Morrow in LCSR to be assigned to a minor adviser to receive guidance about the program. Email: Alison.email@example.com
- When possible, you will be assigned an adviser in your department (though this is not required).
- Students who decide to pursue the minor should also review their academic transcript with their minor adviser to ensure they will be able to complete the requirements.
- Fill out and submit an Add Minor form (which can be obtained from the registrar’s office).
- Complete the Requirements Checkout tables in the Check Out sheet, downloadable from https://lcsr.jhu.edu/robotics-minor/. You should meet with your minor adviser periodically (at least once per year), bringing a copy of this form for review.
- During your senior year, you must also note the Robotics Minor on your Application for Graduation.
- When all requirements have been completed, take the completed form to the Alison Morrow for review and signature.
Undergraduates interested in completing the minor must be assigned a minor adviser. The adviser is responsible for helping the student choose courses and helps to ensure all requirements for the minor are met. The minor advisers are listed on the Robotics Minor website (https://lcsr.jhu.edu/robotics-minor/).
The minor is managed by faculty of the LCSR in collaboration with academic departments and centers of the Whiting School of Engineering. If you have suggestions / questions regarding the minor, please direct them to Prof. Louis Whitcomb at firstname.lastname@example.org.
Undergraduates qualify for the minor provided they have taken at least 18 credits (at the 300-level or above, with a C- or above) from an approved list of courses available below and at https://lcsr.jhu.edu/robotics-minor/ with the following requirements and restrictions:
- Between 6 and 12 credits chosen to cover the three core skills (R, S, C).
- At least 6 credits chosen from advanced supplementary skills (Sup).
- At least 3 credits of the 18 must be a laboratory course (Lab) (at least 15 hours of laboratory time that includes working with physical hardware and/or real data).
At most 3 credits of the 18 can be an independent research or individual study with a faculty member on the list of approved faculty advisers.
- At least 6 credits must be primarily listed in a department other than the student’s home department (it is acceptable if such a course is cross-listed in the student’s home department).
- At most one course up to 3 credits (including independent research or individual study) may be taken S/U, but all other courses must be taken for a letter grade.
Graduate levels of the same course may be substituted for the undergraduate levels listed below without additional permissions.
|EN.520.353 Control Systems||X|
|EN.520.412 Machine Learning for Signal Processing||X|
|EN.520.414 Image Processing & Analysis||X||X|
|EN.520.415 Image Process & Analysis II||X||X|
|EN.520.417 Computation for Engineers||X|
|EN.520.424 FPGA Synthesis Lab||X||X|
|EN.520.432 Medical Imaging Systems||X||X|
|EN.520.433 Medical Image Analysis||X||X|
|EN.520.435 Digital Signal Processing||X||X|
|EN.520.448 Electronics Design Lab||X||X||X|
|EN.520.454 Control Systems Design||X||X||X|
|EN.520.601 Linear Systems Theory||X|
|EN.520.612 Machine Learning for Signal Processing||X|
|EN.530.420 Robot Sensors/Actuators||X||X||X|
|EN.530.424 Dynamics of Robots and Spacecraft||X||X|
|EN.530.446 Robot Devices, Kinematics, Dynamics, and Control||X||X||X|
|EN.530.470 Space Vehicle Dynamics & Control||X||X|
|EN.530.475 Locomotion I: Mechanics||X||X|
|EN.530.476 Locomotion in Mechanical and Biological Systems||X||X|
|EN.530.486 Mechanics of Locomotion||X||X|
|EN.530.603 Applied Optimal Control||X||X||X||X|
|EN.530.678 Nonlinear Control and Planning in Robotics||X||X||X||X|
|EN.530.682 Haptic Applications||X||X||X||X|
|EN.530.707 Robot System Programing||X||X||X||X|
|EN.550.493 Mathematical Image Analysis||X||X||X|
|EN.580.471 Principles of Design of BME Instrumentation||X||X|
|EN.580.472 Medical Imaging Systems||X||X|
|EN.580.571 Honors Instrumentation||X||X||X|
|EN.601.455 Computer Integrated Surgery I||X||X||X||X|
|EN.601.456 Computer Integrated Surgery II||X||X||X||X|
|EN.601.461 Computer Vision||X||X||X|
|EN.601.463 Algorithms for Sensor-Based Robotics||X||X||X|
|EN.601.464 Artificial Intelligence||X||X|
|EN.601.475 Machine Learning||X||X|
|EN.601.476 Machine Learning: Data to Models||X||X|
|EN.601.491 Human-Robot Interaction||X|
|EN.601.682 Machine Learning: Deep Learning||X||X|
|EN.601.760 FFT in Graphics & Vision||X||X||X|