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Ιδρυματικό Αποθετήριο [SANDBOX]
Πολυτεχνείο Κρήτης
Πολυτεχνείο Κρήτης
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| URI: | http://purl.tuc.gr/dl/dias/E4EDB338-B94A-42D0-A83E-E0AAF79C7284 | ||
| Έτος | 2022 | ||
| Τύπος | Πλήρης Δημοσίευση σε Συνέδριο | ||
| Άδεια Χρήσης |
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| Βιβλιογραφική Αναφορά | K. S. Platanitis, G. P. Kladis, E. Petrongonas, C. Skliros, N. C. Tsourveloudis and A. D. Zagorianos, "UAV path planning for offshore swarm-based missions," in Proceedings of the 2022 International Conference on Unmanned Aircraft Systems (ICUAS 2022), Dubrovnik, Croatia, 2022, pp. 124-133, doi: 10.1109/ICUAS54217.2022.9836094. https://doi.org/10.1109/ICUAS54217.2022.9836094 | ||
| Εμφανίζεται στις Συλλογές |
A key feature trend in Smart Delivery (SD) is the use of swarm robotics. In all swarm applications it is desirable that the group should be able to complete its goal(s) safely and energy efficiently, via a sophisticated decision making mechanism, whilst satisfying constraints and meeting mission’s requirements. This is one of the key objectives of the Artificial Intelligence Robust Offshore Unmanned System (AIROUS) project. In this article the offline swarm based Unmanned Aerial Vehicle (UAV) path planning problem is addressed for real offshore environments whilst enhancing energy requirements. A two-step procedure is adopted for the determination of the energy efficient safe-flyable route that satisfies a-priori defined criteria. By the former step, via principles of mechanics, safe-flyable candidate paths are designed meeting functional/physical limitations of the aerial vehicle and vessel traffic in a cluttered environment. By the latter step, those paths are fed in a Genetic Algorithm (GA) setup, to determine the best solution that fulfils the mission’s objectives. The efficacy of the approach is shown via simulation examples.
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