Course pages 2018–19
Unit: Mobile Robot Systems
This course is only taken by Part II 75% students.
Lecturers: Dr A. Prorok
No. of lectures and practical classes: 16
Prerequisite courses: NST Mathematics, Artificial Intelligence, Algorithms.
Capacity: 40
Aims
This course teaches the foundations of autonomous mobile robots, covering topics such as perception, motion control, and planning. It also teaches algorithmic strategies that enable the coordination of multi-robot systems and robot swarms. The course will feature several practical sessions with hands-on robot programming. The students will undertake mini-projects, which will be formally evaluated through a report and presentation.
Lectures
- Robot motion and control. Kinematics, control models, trajectory tracking.
- Control architectures. Sensor-actuator loops, reactive path planning.
- Sensing. Sensors, perception.
- Localization. Markov localization, environment modeling, SLAM.
- Navigation. Planning, receding horizon control.
- Multi-robot systems I. Centralization vs. decentralization, robot swarms.
- Multi-robot systems II. Consensus algorithms, graph-theoretic methods.
- Multi-robot systems III. Task assignment.
- Multi-robot systems IV. Multi-robot path planning.
Objectives
By the end of the course students should:
- understand how to control a mobile robot;
- understand how a robot perceives its environment;
- understand how a robot plans actions (navigation paths);
- know paradigms of coordination in systems of multiple robots;
- know classical multi-robot problems and their solution methods;
- Know how to use ROS (Robot Operating System, http://www.ros.org).
Recommended reading
Siegwart, R., Nourbakhsh, I.R. & Scaramuzza, D. (2004). Autonomous
mobile robots. MIT Press.
Thrun, S., Wolfram B. & Dieter F. (2005). Probabilistic robotics.
MIT Press.
Mondada, F. & Mordechai B. (2018) Elements of Robotics. Springer
Siciliano, B. & Khatib, O. (2016) Springer handbook of robotics.
Springer.
Mesbahi, M. & Egerstedt, M. (2010) Graph theoretic methods in
multiagent networks. Princeton University Press.