Menu

Advanced UAS Programming

Niveau

Beginner

Lernergebnisse der Lehrveranstaltungen/des Moduls

Upon completing this course, students will be able to: - Understand Swarm Intelligence Principles: Explain the fundamental concepts of swarm intelligence (decentralized control, self-organization, emergent behavior) applicable to drones. - Develop Algorithms for Swarm Coordination: Design and implement algorithms for coordination and collective decision-making among drones in a swarm (formation flying, obstacle avoidance, task allocation). - Utilize Communication Protocols for Swarm Operations: Explain and apply communication protocols facilitating efficient information exchange between swarm drones (considering communication range and bandwidth limitations). - Design Swarm Behaviors for Specific Applications: Customize drone swarm behaviors for specific applications (aerial mapping, surveillance, search and rescue, entertainment shows). - Evaluate and Test Swarm Systems: Evaluate drone swarm performance through simulation and real-world testing, identifying and resolving behavior and functionality issues.

Voraussetzungen der Lehrveranstaltung

Drone Programming

Lehrinhalte

- Introduction to Swarm Intelligence: Basics of swarm intelligence and its application in nature and robotics, including decentralized control and emergent behavior concepts. - Fundamentals of Drone Swarm Programming: Overview of the architecture and programming models used in drone swarm operations, including centralized and decentralized control mechanisms. - Communication Protocols: Understanding communication methods and protocols enabling drones within a swarm to share information and make collective decisions. - Swarm Coordination Algorithms: Detailed examination of algorithms for spatial organization, task allocation, and collision avoidance among drones in a swarm. - Simulation and Modeling: Using simulation software to model drone swarm behavior and test programming strategies in a virtual environment before real-world deployment. - Sensor Fusion and Situational Awareness: Techniques for integrating data from multiple sensors across the swarm to achieve a unified perception of the environment. - Autonomy and Decision-Making: Strategies for achieving autonomous decision-making within drone swarms, adapting to changing conditions and objectives without direct human intervention.

Empfohlene Fachliteratur

- Dong, X., Chen, M., Wang, X., & Gao, F. (2023). Intelligent Coordination of UAV Swarm Systems. MDPI. ISBN: 978-3036586595. - Siciliano, B., & Khatib, O. (Eds.). (2016). Springer Handbook of Robotics. Springer. ISBN: 978-3319325507. https://doi.org/10.1007/97. - Dada, E. G. (2017). Swarm Robotics Cooperative Movement Control Using PSO & IPM Algorithms. Lambert Academic Publishing. ISBN: 978-3659799907.

Bewertungsmethoden und -Kriterien

Project and documentation

Unterrichtssprache

Englisch

Anzahl der zugewiesenen ECTS-Credits

5

E-Learning Anteil in %

15

Semesterwochenstunden (SWS)

2.5

Geplante Lehr- und Lernmethode

Presentation, group work, discussion, exercises

Semester/Trisemester, In dem die Lehrveranstaltung/Das Modul Angeboten wird

3

Name des/der Vortragenden

Studienjahr

Kennzahl der Lehrveranstaltung/des Moduls

3_1

Art der Lehrveranstaltung/des Moduls

Integrierte Lehrveranstaltung

Art der Lehrveranstaltung

Pflichtfach

Praktikum/Praktika