Please note! Course description is confirmed for two academic years (1.8.2018-31.7.2020), which means that in general, e.g. Learning outcomes, assessment methods and key content stays unchanged. However, via course syllabus, it is possible to specify or change the course execution in each realization of the course, such as how the contact sessions are organized, assessment methods weighted or materials used.
The student understands and can explain main concepts related to Autonomous mobile robots and vehicles. The student can implement algorithms for different functions of mobile robots.
Schedule: 02.03.2021 - 07.04.2021
Teacher in charge (valid 01.08.2020-31.07.2022): Arto Visala
Teacher in charge (applies in this implementation): Arto Visala
Contact information for the course (applies in this implementation):
CEFR level (applies in this implementation):
Language of instruction and studies (valid 01.08.2020-31.07.2022):
Teaching language: English
Languages of study attainment: English
CONTENT, ASSESSMENT AND WORKLOAD
The locomotion and kinematics of mobile robots and intelligent vehicles. Machine perception and sensors for mobile robots; representing uncertainty, wheel/motor/heading sensors, inertial measurement unit (IMU), beacons, active ranging and machine vision for outdoor use. Mobile robot localization and mapping, probabilistic and other map representations, different approaches for SLAM. Path and trajectory planning and navigation, reactive control, obstacle avoidance and safety. Motion Control; trajectory and path following, NMPC. Intelligent autonomous heavy duty work machines and vehicles. Fleet control. Autonomous cars.
Assessment Methods and Criteria
To pass: Normal Exam, testing understanding + Pass, possibly extra points if well done, of Team works: Design task and ROS/Pioneer, testing ability to apply.
Grading: Basic Grading on the basis of the Exam, max 36 points + Team: Design task, 3 points up to about 50% of one grade improvement & Team: ROS/Pioneer, 3 points up to about 50% of one grade improvement
Contact teaching, lectures: 24 h; Working at home with lecture material 24 h; Team: Design task, 26 h; Team: ROS/Pioneer, 26 h; Reading for exam 35 h
Total 135 h
Lectures and all other material in Mycourses. Alonzo Kelly, CMU, Mobile Robotics: Mathematics Models and Methods, Cambridge University Press, 2014; Trun & al, Probabilistic robotics, MIT Press 2005; Siegwart, Nourbakhsh, Introduction to Autonomous Mobile Robots, MIT Press (2nd ed.)
Basic knowledge of programming, automation and control engineering, robotics and estimation.