Autonomous Systems
Niveau
Beginner
Learning outcomes of the courses/module
Upon completing this course, students will be able to:
- Demonstrate a Solid Foundation in Autonomous Navigation: Describe the principles of autonomous navigation, including different strategies such as waypoint navigation, visual navigation, and SLAM. Apply these strategies to various autonomous system applications.
- Design and Implement Path Planning Algorithms: Design and implement various path planning algorithms, including grid-based, graph-based, and sampling-based methods; generate optimal paths for autonomous systems in complex environments.
- Integrate Sensory Inputs for Navigation: Master data integration from multiple sensors such as LiDAR, GPS, IMU, and cameras and facilitate accurate localization, mapping, and navigation of autonomous systems using this data.
- Apply SLAM Techniques: Understand and apply Simultaneous Localization and Mapping (SLAM) techniques; Enable autonomous systems to build and navigate maps of their environments.
- Develop Obstacle Detection and Avoidance Mechanisms: Design real-time mechanisms for detecting and avoiding obstacles; ensure safe navigation of autonomous systems in dynamic environments.
- Evaluate Navigation Strategies in Different Contexts: Assess the strengths and limitations of various navigation and path planning strategies; apply these strategies in different contexts, such as urban environments, indoor spaces, and off-road terrain.
- Demonstrate a Solid Foundation in Autonomous Navigation: Describe the principles of autonomous navigation, including different strategies such as waypoint navigation, visual navigation, and SLAM. Apply these strategies to various autonomous system applications.
- Design and Implement Path Planning Algorithms: Design and implement various path planning algorithms, including grid-based, graph-based, and sampling-based methods; generate optimal paths for autonomous systems in complex environments.
- Integrate Sensory Inputs for Navigation: Master data integration from multiple sensors such as LiDAR, GPS, IMU, and cameras and facilitate accurate localization, mapping, and navigation of autonomous systems using this data.
- Apply SLAM Techniques: Understand and apply Simultaneous Localization and Mapping (SLAM) techniques; Enable autonomous systems to build and navigate maps of their environments.
- Develop Obstacle Detection and Avoidance Mechanisms: Design real-time mechanisms for detecting and avoiding obstacles; ensure safe navigation of autonomous systems in dynamic environments.
- Evaluate Navigation Strategies in Different Contexts: Assess the strengths and limitations of various navigation and path planning strategies; apply these strategies in different contexts, such as urban environments, indoor spaces, and off-road terrain.
Prerequisites for the course
None
Course content
- Introduction to Autonomous Systems: Overview of various autonomous systems, including ground vehicles, aerial drones, and maritime vehicles. Exploring the scope and challenges of autonomy.
- Fundamentals of Path Planning: Introduction to concepts and algorithms used in path planning, such as grid-based, graph-based, and sampling-based methods. Discussion on A*, Dijkstra’s algorithm, RRT (Rapidly-exploring Random Tree), and their variations.
- Localization and Mapping: Techniques for determining the system's position within its environment and creating maps. Discussion on SLAM (Simultaneous Localization and Mapping) and its variants, including visual SLAM and LiDAR-based approaches.
- Navigation and Obstacle Avoidance: Strategies for autonomous navigation in dynamic environments, including static and moving obstacle avoidance. Overview of reactive and predictive models for safe navigation.
- Machine Learning and AI in Autonomy: Exploration of the role of machine learning and artificial intelligence in enhancing the capabilities of autonomous systems, including decision-making, object detection, and adaptive path planning.
- Control Systems for Autonomous Operation: Basics of control theory as applied to autonomous systems, including PID control, feedforward control, and state feedback control. Discussion on how these systems execute planned paths and maintain stability.
- Fundamentals of Path Planning: Introduction to concepts and algorithms used in path planning, such as grid-based, graph-based, and sampling-based methods. Discussion on A*, Dijkstra’s algorithm, RRT (Rapidly-exploring Random Tree), and their variations.
- Localization and Mapping: Techniques for determining the system's position within its environment and creating maps. Discussion on SLAM (Simultaneous Localization and Mapping) and its variants, including visual SLAM and LiDAR-based approaches.
- Navigation and Obstacle Avoidance: Strategies for autonomous navigation in dynamic environments, including static and moving obstacle avoidance. Overview of reactive and predictive models for safe navigation.
- Machine Learning and AI in Autonomy: Exploration of the role of machine learning and artificial intelligence in enhancing the capabilities of autonomous systems, including decision-making, object detection, and adaptive path planning.
- Control Systems for Autonomous Operation: Basics of control theory as applied to autonomous systems, including PID control, feedforward control, and state feedback control. Discussion on how these systems execute planned paths and maintain stability.
Recommended specialist literature
- Barfoot, T. D. (2024). State Estimation for Robotics. Cambridge University Press. ISBN: 978-1009299909. https://doi.org/10.1017/9781009299909
- LaValle, S. M. (2006). Planning Algorithms. Cambridge University Press. ISBN: 978-0521862059.
- Siegwart, R., Nourbakhsh, I. R., & Scaramuzza, D. (2011). Introduction to Autonomous Mobile Robots (2nd ed.). The MIT Press. ISBN: 978-0262015356.
- LaValle, S. M. (2006). Planning Algorithms. Cambridge University Press. ISBN: 978-0521862059.
- Siegwart, R., Nourbakhsh, I. R., & Scaramuzza, D. (2011). Introduction to Autonomous Mobile Robots (2nd ed.). The MIT Press. ISBN: 978-0262015356.
Assessment methods and criteria
Exam
Language
English
Number of ECTS credits awarded
5
Share of e-learning in %
15
Semester hours per week
2.5
Planned teaching and learning method
Presentation, group work, discussion, exercises
Semester/trimester in which the course/module is offered
3
Name of lecturer
Academic year
Key figure of the course/module
3_5
Type of course/module
integrated lecture
Type of course
Compulsory