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.
- 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.
- 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.
- 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