Estudia

• CG1 Organize and evaluate the learning and the research activity themselves and develop strategies to improve them.
• CG1- Organize and evaluate the learning and the research activity themselves and develop strategies to improve them
• CB6 Possess and understand the knowledge that provides a basis or opportunity to be original in the development and/or application of ideas, often in a research context.
• CB6- Possess and understand the knowledge that provides a basis or opportunity to be original in the development and/or application of ideas, often in a research context
• CB8 That students are able to integrate knowledge and face the complexity of making judgments based on information that, being incomplete or limited, includes reflections on social and ethical responsibilities linked to the application of their knowledge and judgments.
• CB8- That students are able to integrate knowledge and face the complexity of making judgments based on information that, being incomplete or limited, includes reflections on social and ethical responsibilities linked to the application of their knowledge and judgments
• CB10 That students have the learning skills to allow them to continue studying in a way that will mostly be self-directed or autonomous.
• CB10- That students have the learning skills to allow them to continue studying in a way that will mostly be self-directed or autonomous
• CE1 Programming, at an advanced level, in the languages and libraries most used in intelligent field robotics.
• CE1- Programming, at an advanced level, in the languages and libraries most used in intelligent field robotics
• CE2 Analyse a problem related to intelligent autonomous systems and identify the appropriate techniques and tools to solve it.
• CE2- Analyse a problem related to intelligent autonomous systems and identify the appropriate techniques and tools to solve it
• CE5 Know, understand and be able to apply the algorithms that allow autonomous vehicles to localize themselves and navigate effectively.
• CE5- Know, understand and be able to apply the algorithms that allow autonomous vehicles to localize themselves and navigate effectively
• CE6 Know and understand when and how to use the main sensors and actuators available for intelligent field robots.
• CE6- Know and understand when and how to use the main sensors and actuators available for intelligent field robots
• CE8 Understand the mathematical foundations of intelligent robotic system algorithms.
• CE8- Understand the mathematical foundations of intelligent robotic system algorithms

1. Introduction

2. Bayes Filter

3. Non-Parametric Filters: Histogram Filter; Particle Filter

4. Occupancy Grid Mapping

5. Parametric Filters: Kalman Filter, Extended Kalman Filter

6. Map Based Localization

• Pere Ridao (2022). Probabilistic Robot Localization. Book Draft. Not yet published.
• Sebastian Thrun, Wolfram Burgard, Dieter Fox (2005). Probabilistic Robotics. Mit Press.
• Peter Corke (2011). Robot Vision and control. Springer.

Assessment activities:

5% Tests
6% MATLAB programming Exercices
45% Laboratory Exercices
44% Theory & Exercices. Evaluated through continuous evaluations examination, plus a final evaluation.

Specific criteria for the "No show" grade:
When anyone of the parts is not submitted.

Single Assessment:
The same evaluation activities will be carried out but facilitating that those activities that require a compulsory presence in the laboratory could be done either in person at agreed times, or remotely using robot simulators. Deadlines will also be adjusted so that a single delivery of all activities can be made.

Minimum requirements to pass:
Every part (Test, MATLAB, Lab & Examination) must have a mark beyond 5 to pass the course.

Appointments with the professors to solve doubts may be requested either in person during the lectures or lab courses o by email.

The primary means of communications with the students is: 1) in person during the lectures and lab classes, 2) through the moodle course page and 3) through email.

Knowledge of phyton and MATLAB is assumed. This programming language will not be taught. Although it is possible to complete the lab work with the laboratory computers, it is recommended to bring your own laptop to the lab to make it easier to complete the work at home. A virtual machine with ubuntu, ROS and the Turtlebot SDK will be provided.

There will be a Turtlebot available for every 2 students.