Students master the main principles of mobile robotics and gain basic knowledge for design and development of mobile robots control and navigation systems.
- clasify mobile robots according to various criteria
- analyze driving mehanisms and sensor system sutable for intended application
- assembly sensors and actuators with the embedded computer system on mobile robot
- develop sensor fusion algorithms
- develop motion planning algorithms
- develop motion of mobile robots localization
- develop algorithms of environment 2D map building
Forms of Teaching
Lectures are organized in two cycles.Laboratory Work
Students work five laboratory exercises (LE) on LEGO mobile robots - each LV lasts three hours : • LE1: Odometry calibration of a mobile robot • LE2: Occupancy grid map building by applying Bayes rule • LE3: Mobile robot localization by Kalman filter and sonars • LE4: Path planning of a mobile robot • LE5: Path following and obstacle avoidanceConsultations
|Type||Threshold||Percent of Grade||Threshold||Percent of Grade|
|Laboratory Exercises||50 %||20 %||50 %||20 %|
|Mid Term Exam: Written||50 %||30 %||0 %|
|Final Exam: Written||50 %||30 %|
|Final Exam: Oral||20 %|
|Exam: Written||50 %||60 %|
|Exam: Oral||20 %|
All five laboratory exercises are obligatory.
Week by Week Schedule
- General considerations regarding mobile robots: basic terms, definitions, classifications, historical development, applications and examples of mobile robots.
- Mobile robots hardware, drive mechanisms, actuators. Mobile robots locomotion.
- Mobile robots kinematics.
- Proprioceptive and non-visual perceptive sensors for mobile robots.
- Visual perceptive sensors for mobile robots.
- Processing and interpretation of robots sensors signals. Measurement uncertainty.
- Multiple sensors information fusion in order to improve quality and robustness of robots navigation through space.
- Midterm exam.
- Control and navigation system structures.
- Algorithms for global path planning of mobile robot in space.
- Algorithms for obstacle avoidance and global path following.
- Robots relative and absolute localization in space.
- Environment modeling: occupancy grid maps, geometrical properties maps, topological maps, hybrid maps.
- Introduction to self-learning mobile robots and human-robot communication. Basics of coordinated work of multiple autonomous mobile robots.
- Final exam.