- Titel
- Practical implementation of safe human-drone interaction: curve-shortening flow method for UAV evasion in hovering positions
- Zitierfähige Url:
- https://nbn-resolving.org/urn:nbn:de:bsz:l189-qucosa2-957745
- Konferenz
- 21. AALE-Konferenz. Dresden, 12. März - 14. März 2025
- Quellenangabe
- Tagungsband AALE 2025
; menschzentrierte Automation im digitalen Zeitalter
Herausgeber: Hochschule für Technik, Wirtschaft und Kultur Leipzig
Erscheinungsort: Leipzig
Erscheinungsjahr: 2025
Seiten: 39-48
ISBN: 978-3-910103-05-4 - Erstveröffentlichung
- 2025
- DOI
- https://doi.org/10.33968/2025.06
- Abstract (EN)
- Especially in high-wage countries, the increasing diversity of product customization requires flexible production systems. Indoor unmanned aerial vehicles (UAVs) offer considerable potential for material transport due to their minimal infrastructure requirements and the use of otherwise unused airspace. However, their safe operation in close proximity to humans still remains a challenge. At the same time, there are a wide variety of path planning algorithms to tackle this problem. One path planning algorithm that stands out for operating indoor UAVs is the curve-shortening flow method (CFM), which ensures safe and robust path planning due to its computational efficiency and numerically stable solvability. This paper examines the suitability of the CFM for static evasion scenarios. A static evasion scenario refers to an UAV in a predetermined static hovering position that automatically avoids approaching obstacles to prevent collisions and then returns to its previously commanded position. Three approaches to initializing the CFM are evaluated via simulations and real experiments. The results show that it is possible to use the CFM for static evasion scenarios without major adjustments. These findings pave the way for exploration of more complex implementations in dynamic and multiagent environments.
- Freie Schlagwörter (EN)
- Robotics, Unmanned Aerial Vehicle (UAV), Path Planning, Curve-Shortening Flow Method (CFM), Smart Factory
- Herausgeber (Institution)
- Hochschule für Technik, Wirtschaft und Kultur Leipzig, Leipzig
- Version / Begutachtungsstatus
- publizierte Version / Verlagsversion
- URN Qucosa
- urn:nbn:de:bsz:l189-qucosa2-957745
- Veröffentlichungsdatum Qucosa
- 18.02.2025
- Dokumenttyp
- Konferenzbeitrag
- Sprache des Dokumentes
- Englisch
- Lizenz / Rechtehinweis
CC BY 4.0