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Department of Computer Science and Technology

Principal lecturer: 
Dr Amanda Prorok
Students: 
Part II CST 75%
Course code: 
MobRobot
Hours: 
12
Class limit: 
48

This module can accommodate up to 48 students consisting of both Part II students and MPhil / Part III Students

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.
  • Robot learning. Introduction to reinforcement learning for autonomous decision-making.

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. and Scaramuzza, D. (2004). Autonomous mobile robots. MIT Press.
Thrun, S., Wolfram B. and Dieter F. (2005). Probabilistic robotics. MIT Press.
Mondada, F. and Mordechai B. (2018) Elements of Robotics. Springer
Siciliano, B. and Khatib, O. (2016) Springer handbook of robotics. Springer.
Mesbahi, M. and Egerstedt, M. (2010) Graph theoretic methods in multiagent networks. Princeton University Press.