MSc:SensingPerceptionActuation old

Sensing, Perception & Actuation

• Course name: Sensing, Perception & Actuation
• Course number: R-05
• Area of instruction: Computer Science and Engineering

Administrative details

• Faculty: Computer Science and Engineering
• Year of instruction: 1st year of MSc
• Semester of instruction: 1st semester
• No. of Credits: 5 ECTS
• Total workload on average: 180 hours overall
• Frontal lecture hours: 2 hours per week.
• Frontal tutorial hours: 0 hours per week.
• Lab hours: 2 hours per week.
• Individual lab hours: 2 hours per week.
• Frequency: weekly throughout the semester.
• Grading mode: letters: A, B, C, D.

Course outline

This course covers selected topics in sensors and sensing area, which are in particular important for robotic application. The students are expected to learn the course topics on their own beyond the level of the lectures. The goals throughout the course are to refresh students’ math skills in data and error analysis, and familiarize them with sensing principles and sensor utilization, giving them both analytic and experimental experience. The students will be required to participate in laboratory practicum and solve practical tasks on data processing in MATLAB environment.

Expected learning outcomes

• Be acquainted with and utilize main sensors for robotic applications.
• Be familiar with Sensing principles, Measurements and error analysis, Data analysis, Sensor calibration, etc.
• Be able to write a code for sensor data processing in MATLAB.
• Train engineering skills during lab. tests, studying sensor calibration and utilization, and using relevant software for sensor data capture, export, interpretation and representation.

Required background knowledge

• XXX

Prerequisite courses

• Physics I
• Physics II
• Mathematical Analysis I
• Mathematical Analysis II
• Analytic Geometry and Linear Algebra I
• Analytic Geometry and Linear Algebra II
• Probability and Statistics

Detailed topics covered in the course

The topics below are presented with the granularity of at most the academic hour of instruction. For each topic it is specified if it an Introduction to the topic, a Deep explanation, or a Review of a subject already covered in another course.

• Introduction to sensors: Sensors’ classification, Characteristics, Dynamic range Accuracy
• Introduction to Measurements and Error Analysis
• Introduction to Data Analysis: Linear Regression, Least-Squares Fitting, Curve fitting, and Filtering
• Image sensors: camera matrix, characteristics and calibration
• Video camera: CCTV, IR & thermal imaging camera, Fish eye camera
• Stereo vision: Stereosystem, Stereogeometry, 3D reconstruction
• Depth, TOF, RGBD camera, MS Kinect: characteristics and calibration
• Review and Midterm
• Sensor fusion (principles), Multisensory, multicamera, MoCap systems
• LIDAR: Laser rangefinders. Laser-camera systems
• SONAR. Doppler radar. Acoustic sensor systems. Sound spectrogram
• Inertial sensors: IMU, accelerometers, gyroscopes, Magnetic Compasses, GPS
• Internal sensors: position, velocity, torque & force sensors, encoders
• MEMS for robot applications. Smart and Intelligent Sensors

Reference material

• Slides will be provided during the course

Required computer resources

Students are required to have laptops.

Evaluation

• Home assignments (25%)
• Mid-term exam (15%)
• In-class activity, quizzes and lab. practicum (15%)
• Final exam (25%)
• Project (20%)