Autonomous robotics – B(E)3M33ARO1

!!! Prerequisites !!!

It is assumed that students of this course have a working knowledge of the following concepts:

  • Mathematical analysis (B0B01MA2): gradient, Jacobian, Hessian, multidimensional Taylor polynomial
  • Optimization (B0B33OPT): Gauss-Newton method, Levenberg Marquardt method, full Newton method
  • Linear algebra (B0B01LAG): pseudo-inverse, SVD decomposition, least-squares method
  • Probability theory (B0B01PST): multivariate gaussian probability, Bayes theorem
  • Statistics (B0B01PST): maximum likelihood and maximum aposteriori estimate
  • Robotics (B3B33ROB): coordinate frames, Euclidean transformation and camera model.
  • Programming (B3B33ALP + B3B36PRG): python + linux

Course overview

The Autonomous robotics course will explain the principles needed to develop algorithms for mobile robots. In particular, the main focus is on the perception-action cycle:

  • Perception: Simultaneous Localization and Mapping (SLAM) that estimates a metrical map (e.g. pointcloud map, occupancy grid) of the world and the robot's location from online measurements (camera or lidar, accelerometers, gyroscopes, GPS, radio beacons) and localization of markers in the map.
  • Action: Planning the trajectory that optimizes a user-specified exploration criterion given the estimated map and the robot's location and performing the plan in the real world.

The course consists of lectures and labs. Lectures take place in KN:E-107 every Monday at 11:00. Labs take place in KN:E-132, the time slot corresponds to the code of your course.

  • Lectures: Some lecture are accompanied by a PDF worksheet with numerical problems to be solved by students (we do not check if students do so, however the individual solution of provided problems is highly recommended in order to pass the exam test).
  • Labs: The first 10 weeks will be focused on regular labs, the rest is devoted to solving the semestral work. Some of the regular labs contain homework to be solved before the start of the following labs.
  • Semestral work is scheduled for the second half of the term. Students will start to solve the semestral work in the Gazebo simulator and then submit the work on a real robot.

Points, credit requirements and final grade

Maximum number of points is 100. Points are structured as follows:

  • homework (30 total),
  • semestral work (10 simulation + 10 practical),
  • exam test (50).

Minimum credit requirements:

  • Upload own solution of all homework (which satisfy minimum requirements) before the beginning of the labs in the thirteenth week.
  • Upload own solution of the semestral work for simulation evaluation.
  • Demonstration of the semestral work on a real robot.
  • Active participation on all regular labs. Active participation means that students are able to demonstrate progress on the lab assignment and answer questions of the lab tutor.

The final grade will be determined by the total number of points according to the following table

No of points Exam assessment
0-49 F
50- 59 E
60-69 D
70-79 C
80-89 B
90-100 A
  1. Thrun et al. Probabilistic robotics, MIT press 2017,[pdf]
  2. Hartley, Zisserman Multipleview Geometry, 2004,
  3. Steven M. LaValle. Planning Algorithms, Cambridge University Press, 2006. (free online,


We want students to work individually, therefore any plagiarism in codes, homework or reports will be mercilessly punished ;-). We strongly urge each student to read what is/isnot a plagiarism - we believe that many students will be surprised. In any case, it is not permitted to use the work of your colleagues or predecessors. Each student is responsible for ensuring that his work does not get into the hands of other colleagues. In the case of multiple submission of the same work, all involved students will be penalized, including those who gave the work available to others

courses/aro/start.txt · Last modified: 2024/02/20 14:05 by nekovfra