{"id":null,"code":"DTEK2083","name":{"valueFi":"Perception and Navigation in Mobile Robotics","valueEn":"Perception and Navigation in Mobile Robotics","valueSv":"Perception and Navigation in Mobile Robotics"},"credits":5.0,"minCredits":5,"maxCredits":5,"tags":[{"code":"KEKE","title":{"valueFi":"Kestävä kehitys","valueEn":"Sustainable development","valueSv":"Sustainable development"}}],"createdAt":1790534320762,"contentList":[{"title":{"valueFi":"Osaamistavoitteet","valueEn":"Learning outcomes","valueSv":""},"content":{"valueFi":"Upon completion of this course, students will be able to understand the different types of sensors utilized in autonomous mobile robots and other types of autonomous systems. Students will be able to visualize and process different types of data from a variety of sensors (e.g., images, depth maps, point clouds, radar data or data from inertial measurement units) for different types of perception (e.g., visual, lidar, radar or wireless ranging, among others). Students will learn to design and analyze solutions that enable mobile robots to navigate, map and sense their environment, and take action based on the changing conditions. \r\n \r\nThe course strengthens the following working life skills: presentation skills, creativity, problem solving skills and information and communication technology skills.","valueEn":"Upon completion of this course, students will be able to understand the different types of sensors utilized in autonomous mobile robots and other types of autonomous systems. Students will be able to visualize and process different types of data from a variety of sensors (e.g., images, depth maps, point clouds, radar data or data from inertial measurement units) for different types of perception (e.g., visual, lidar, radar or wireless ranging, among others). Students will learn to design and analyze solutions that enable mobile robots to navigate, map and sense their environment, and take action based on the changing conditions. \r\n \r\nThe course strengthens the following working life skills: presentation skills, creativity, problem solving skills and information and communication technology skills.","valueSv":"Upon completion of this course, students will be able to understand the different types of sensors utilized in autonomous mobile robots and other types of autonomous systems. Students will be able to visualize and process different types of data from a variety of sensors (e.g., images, depth maps, point clouds, radar data or data from inertial measurement units) for different types of perception (e.g., visual, lidar, radar or wireless ranging, among others). Students will learn to design and analyze solutions that enable mobile robots to navigate, map and sense their environment, and take action based on the changing conditions. \r\n \r\nThe course strengthens the following working life skills: presentation skills, creativity, problem solving skills and information and communication technology skills."}},{"title":{"valueFi":"Sisältö","valueEn":"Content","valueSv":""},"content":{"valueFi":"The course delves into the analysis of predominant mobile robots to provide insight into the sensors, actuators, and perception & navigation systems of autonomous robots by utilizing a versatile simulation environment. The course covers the process of acquiring, processing, analyzing, and distributing data in robotic and autonomous systems. First, the course overviews different types of data and the corresponding sensors. Then, the course expands the simulations of the previous “Algorithmic Foundations of Robotic and AI Systems\" course with exercises and projects involving real-time computer vision, control, and different perception, navigation, and planning algorithms.","valueEn":"The course delves into the analysis of predominant mobile robots to provide insight into the sensors, actuators, and perception & navigation systems of autonomous robots by utilizing a versatile simulation environment. The course covers the process of acquiring, processing, analyzing, and distributing data in robotic and autonomous systems. First, the course overviews different types of data and the corresponding sensors. Then, the course expands the simulations of the previous “Algorithmic Foundations of Robotic and AI Systems\" course with exercises and projects involving real-time computer vision, control, and different perception, navigation, and planning algorithms.","valueSv":"The course delves into the analysis of predominant mobile robots to provide insight into the sensors, actuators, and perception & navigation systems of autonomous robots by utilizing a versatile simulation environment. The course covers the process of acquiring, processing, analyzing, and distributing data in robotic and autonomous systems. First, the course overviews different types of data and the corresponding sensors. Then, the course expands the simulations of the previous “Algorithmic Foundations of Robotic and AI Systems\" course with exercises and projects involving real-time computer vision, control, and different perception, navigation, and planning algorithms."}},{"title":{"valueFi":"Suoritustavat","valueEn":"Study methods","valueSv":""},"content":{"valueFi":"The course involves lectures, simulation-based exercises and/or group projects that together enable students to design and implement sensor-based planning algorithms for a mobile robot, and expose them to practical issues involved in realizing autonomous devices.","valueEn":"The course involves lectures, simulation-based exercises and/or group projects that together enable students to design and implement sensor-based planning algorithms for a mobile robot, and expose them to practical issues involved in realizing autonomous devices.","valueSv":""}},{"title":{"valueFi":"Toteutustavat","valueEn":"Course unit methods","valueSv":""},"content":{"valueFi":"Lectures, exercises, and independent work.","valueEn":"Lectures, exercises, and independent work.","valueSv":""}},{"title":{"valueFi":"Oppimateriaalit","valueEn":"Learning material","valueSv":""},"content":{"valueFi":"Lecture slides, instructions for exercises, reference material","valueEn":"Lecture slides, instructions for exercises, reference material","valueSv":""}},{"title":{"valueFi":"Lisätiedot","valueEn":"Further information","valueSv":""},"content":{"valueFi":"The content of the course has a connection to the following UN sustainable development goals: 4. Quality education, 7. Affordable and clean energy, 8. Decent work and economic growth, 9. Industry, innovation and infrastructure, 11. Sustainable cities and communities, 12. Responsible consumption and production, 13. Climate action, 14. Life below water, 15. Life on land and 17. Partnerships for the goals.","valueEn":"The content of the course has a connection to the following UN sustainable development goals: 4. Quality education, 7. Affordable and clean energy, 8. Decent work and economic growth, 9. Industry, innovation and infrastructure, 11. Sustainable cities and communities, 12. Responsible consumption and production, 13. Climate action, 14. Life below water, 15. Life on land and 17. Partnerships for the goals.","valueSv":""}},{"title":{"valueFi":"Kurssikirjallisuus","valueEn":"Literature","valueSv":""},"content":{"valueFi":"","valueEn":"","valueSv":""}},{"title":{"valueFi":"Esitietovaatimukset","valueEn":"Qualifications","valueSv":""},"content":{"valueFi":"The course \"Algorithmic Foundations of Robotic and AI Systems\" or comparable knowedge is a prerequisite, in addition to Python programming and basic algorithmic design.","valueEn":"The course \"Algorithmic Foundations of Robotic and AI Systems\" or comparable knowedge is a prerequisite, in addition to Python programming and basic algorithmic design.","valueSv":""}},{"title":{"valueFi":"Arviointiasteikko","valueEn":"Assessment scale","valueSv":""},"content":{"valueFi":"0-5","valueEn":"0-5","valueSv":"0-5"}},{"title":{"valueFi":"Arviointikriteerit","valueEn":"Assessment criteria","valueSv":""},"content":{"valueFi":"","valueEn":"","valueSv":""}},{"title":{"valueFi":"Arviointikriteerit 2","valueEn":"Assessment criteria 2","valueSv":""},"content":{"valueFi":"","valueEn":"","valueSv":""}},{"title":{"valueFi":"Arviointikriteerit 3","valueEn":"Assessment criteria 3","valueSv":""},"content":{"valueFi":"","valueEn":"","valueSv":""}},{"title":{"valueFi":"Arviointikriteerit 4","valueEn":"Assessment criteria 4","valueSv":""},"content":{"valueFi":"","valueEn":"","valueSv":""}},{"title":{"valueFi":"Kielet","valueEn":"Languages","valueSv":""},"content":{"valueFi":"englanti","valueEn":"English","valueSv":"engelska"}},{"title":{"valueFi":"Taso","valueEn":"Level","valueSv":""},"content":{"valueFi":"Syventävät opinnot","valueEn":"Advanced Studies","valueSv":""}},{"title":{"valueFi":"Oppiaine","valueEn":"Subject","valueSv":""},"content":{"valueFi":"Tietotekniikka","valueEn":"Information and Communication Technology","valueSv":"Information and Communication Technology"}},{"title":{"valueFi":"Vastuuhenkilöt","valueEn":"Person in charge","valueSv":""},"content":{"valueFi":"Jens Lundell","valueEn":"Jens Lundell","valueSv":"Jens Lundell"}},{"title":{"valueFi":"Luokittelu","valueEn":"Classification","valueSv":""},"content":{"valueFi":"Kestävä kehitys","valueEn":"Sustainable development","valueSv":"Sustainable development"}},{"title":{"valueFi":"Linkit","valueEn":"Links","valueSv":""},"content":{"valueFi":"","valueEn":"","valueSv":""}}]}