Probabilistics Robotics


Content, progress and pedagogy of the module

The module is based on knowledge achieved in Probability Theory, Stochastic Processes and Applied Statistics.

Learning objectives


  • Have comprehension of the benefits of using a probabilistic approach to design robotic systems
  • Have comprehension of the probabilistic approaches used in modern robotic systems
  • Have comprehension on Markov processes and Bayesian filtering
  • Have fundamental knowledge on decision making systems, Markov Decision Processes and Partially Observable Markov Decision Processes
  • Have fundamental knowledge on state estimation, sensor fusion as well as planning and control
  • Have fundamental knowledge of reinforcement learning and end-to-end systems
  • Have comprehension on robot kinematics and dynamics


  • Be able to analyse state estimation techniques such as Kalman filtering, particle filtering
  • Be able to select the appropriate state estimation and sensor fusion methods in a real-world application in robotics
  • Be able to use Markov or Bayesian localization methods
  • Be able to model how a dynamic robotic system interacts within an uncertain environment
  • Be able to use computer vision and/or odometry to make a robot navigate in an uncertain environment


  • Be able to design, implement, and validate probabilistic models that can represent uncertainty and variability in the environment and the robot's perception.
  • Be able to implement state estimation techniques such as Kalman filtering, particle filtering, and Markov localization to estimate the robot's pose, the map of the environment, and other relevant states.
  • Be able to design planning and control algorithms that can optimize the behavior of robots while they navigate in uncertain and dynamic environments.
  • Be able to use simulation environments to test probabilistic robotic systems
  • Be able to use programming frameworks and libraries to design probabilistic robotic systems

Type of instruction

Lecture followed by numerical and simulation exercises and possible e-learning activities.

Extent and expected workload

Since it is a 5 ECTS course module, the work load is expected to be 150 hours for the student.



Name of examProbabilistics Robotics
Type of exam
Written or oral exam
Permitted aids
With certain aids:
For more information about permitted aids, please visit the course description in Moodle.
Assessment7-point grading scale
Type of gradingInternal examination
Criteria of assessmentThe criteria of assessment are stated in the Examination Policies and Procedures

Facts about the module

Danish titleSandsynlighedsrobotteknik
Module codeE-AIAS-K2-3
Module typeCourse
Duration1 semester
Language of instructionEnglish
Location of the lectureCampus Esbjerg
Responsible for the module


Education ownerMaster of Science (MSc) in Engineering (Advanced Power Electronics)
Study BoardStudy Board of Build, Energy, Electronics and Mechanics in Esbjerg
DepartmentDepartment of Energy
FacultyThe Faculty of Engineering and Science