IHA-4306 Fundamentals of Mobile Robots, 5 cr

Additional information

Opintojaksolla IHA-4306 Fundamentals of Mobile Robots rajoitamme opiskelijoiden määrää seuraavien periaatteiden mukaan:

- Tutkinnon tavoitteellista suoritusaikaa oltava jäljellä
- DI-tutkinnossa kirjoilla opiskelijat
- Kv-opiskelijoista vain 2016 aloittaneet tutkintorakenteensa vuoksi

. In the course IHA-4306 Fundamentals of Mobile Robots the participation is limited according to the criteria:

Prioritisation:

1. The student's normative time to degree is not finished
2. Students studying in the Master's programme in Automation Engineering
3. Primarily International students who have started their studies in 2016
- students who have started in 2015 or earlier are in the thesis-writing stage of their studies according to the
normative timeline of the degree
- students who have started in 2017 cannot include Robotics in their degree
The course is only intended for degree students

Person responsible

Reza Ghabcheloo, Risto Ritala

Lessons

Learning Outcomes

This course introduces some answers to the basic questions of "where am I?" and "where have I seen?" and "How do I get there?", which are called location, mapping and planning, respectively, in the robotic community. More specifically, - Students will learn basics about range sensors (Lidar, Radar, Sonar), radio based (GNSS, UWB), egomotion sensors (IMUs, wheel odometry) and their noise characteristics and probabilistic modeling. - Students will learn about coordinate frames and sensor kinematics, that is, how to calculate sensor output in different coordinate frames. - Students will learn how to fuse information comming from different sources (sensors, maps, control inputs,etc) using Bayes filters in particular Kalman filters and particle filters, and to use those to localize moving platforms. - Students will learn about basic world model representations and how to build them (map building) from sensor inputs. - Students will learn important deterministic route planning methods: Dynamic programming (DP), Dijkstra, A*, - Student will also learn planning under uncertainty with MDP (Markov Decision Processes) Notes: * The focus of the planning part of the course will be on point robots, to avoid some complications which will rise due to differential kinematics of contact with ground. * Although the focus of examples and presentation is on mobile robots moving on a 2D plane, most of the methods are applicable to higher dimenstions (manipulators or moving in 3D).

Content

Correspondence of content

There is no equivalence with any other courses