BSc: Sensors And Sensing

Sensors and Sensing

• Course name: Sensors and Sensing
• Course number:
• Knowledge area: Hardware; Sensors and actuators; Robotic components.

Course Characteristics

Key concepts of the class

• Physical principles for sensors and sensing
• Basics of error analysis and calibration
• Sensor’s data processing and filtering

What is the purpose of this course?

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/Python/C++ environment.

Course Objectives Based on Bloom’s Taxonomy

What should a student remember at the end of the course?

By the end of the course, the students should be able to remember and recognize

• Various types of sensors, their pros and cons,
• Working principles of electric actuators (DC motors),
• Operation principles of motion transmission mechanisms,
• Fundamentals of linear feedback control systems, and
• Principles of controller design for mechatronic systems.

What should a student be able to understand at the end of the course?

By the end of the course, the students should be able to describe and explain

• How to select sensors for a given application,
• How to choose appropriate transmission mechanisms and account for their efficiency,
• How to integrate all selected parts to create a mechatronic system,
• Typical nonlinearities that originate from electronic and mechanical sources and their effects on system performance, and
• How to tune control system for selected motor and desired performance specifications.

What should a student be able to apply at the end of the course?

By the end of the course, the students should be able to

• Drive differential equations of motion describing behavior of physical systems with several degrees of freedom,
• Calculate motor and sensor requirements for a given physical system, control task or application,
• Select appropriate motor that provides enough power while avoiding overheating,
• Tune control system for selected motor, transmission mechanism and sensor to achieve desired response and stability.

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.

Expected acquired core competences

• Know sensors physical principles and their limitations.
• Be capable to describe sensors work and regions for applications.
• Be familiar with Sensor’s calibration and exploitation.
• Be able to introduce and perform Measurements, Data and Error analysis.
• Be acquainted with and utilize Linear regression, Least-squares fitting, Curve fitting, and Filtering procedures.
• Demonstrate ability to process sensor data by relevant math. methods.
• Write MATLAB code for sensor data processing.

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

Resources and reference material

• Slides will be provided during the course

Required computer resources

Students are required to have laptops.

Course evaluation

Course grade breakdown

		Proposed points
Home assignments	25
In-class activity, quizzes and lab. practicum	15
Final exam	25
Project	20

If necessary, please indicate freely your course’s features in terms of students’ performance assessment.