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Hybrid Evolutionary Approach to Multi-objective Path Planning for UAVs

Hohmann, Nikolas ; Bujny, Mariusz ; Adamy, Jürgen ; Olhofer, Markus (2022):
Hybrid Evolutionary Approach to Multi-objective Path Planning for UAVs. (Postprint)
In: 2021 Symposium Proceedings,
Darmstadt, IEEE, 2021 IEEE Symposium Series on Computational Intelligence (SSCI), Orlando, FL, USA, 05.-07.12.2021, ISBN 978-1-7281-9048-8,
DOI: 10.26083/tuprints-00020386,
[Conference or Workshop Item]

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Item Type: Conference or Workshop Item
Origin: Secondary publication
Status: Postprint
Title: Hybrid Evolutionary Approach to Multi-objective Path Planning for UAVs
Language: English
Abstract:

The goal of Multi-Objective Path Planning (MOPP) is to find Pareto-optimal paths for autonomous agents with respect to several optimization goals like minimizing risk, path length, travel time, or energy consumption. In this work, we formulate a MOPP for Unmanned Aerial Vehicles (UAVs). We utilize a path representation based on Non-Uniform Rational B-Splines (NURBS) and propose a hybrid evolutionary approach combining an Evolution Strategy (ES) with the exact Dijkstra algorithm. Moreover, we compare our approach in a statistical analysis to state-of-the-art exact (Dijkstra's algorithm), gradient-based (L-BFGS-B), and evolutionary (NSGA-II) algorithms with respect to calculation time and quality features of the obtained Pareto fronts indicating convergence and diversity of the solutions. We evaluate the methods on a realistic 2D urban path planning scenario based on real-world data exported from OpenStreetMap. The examination's results indicate that our approach is able to find significantly better solutions for the formulated problem than standard Evolutionary Algorithms (EAs). Moreover, the proposed method is able to obtain more diverse sets of trade-off solutions for different objectives than the standard exact approaches. Thus, the method combines the strengths of both approaches.

Book Title: 2021 Symposium Proceedings
Place of Publication: Darmstadt
Publisher: IEEE
Collation: 8 Seiten
Classification DDC: 000 Allgemeines, Informatik, Informationswissenschaft > 004 Informatik
500 Naturwissenschaften und Mathematik > 510 Mathematik
600 Technik, Medizin, angewandte Wissenschaften > 600 Technik
600 Technik, Medizin, angewandte Wissenschaften > 620 Ingenieurwissenschaften
Divisions: 18 Department of Electrical Engineering and Information Technology > Institut für Automatisierungstechnik und Mechatronik > Control Methods and Robotics
Event Title: 2021 IEEE Symposium Series on Computational Intelligence (SSCI)
Event Location: Orlando, FL, USA
Event Dates: 05.-07.12.2021
Date Deposited: 02 Feb 2022 13:44
Last Modified: 02 Feb 2022 13:44
DOI: 10.26083/tuprints-00020386
Corresponding Links:
URN: urn:nbn:de:tuda-tuprints-203866
URI: https://tuprints.ulb.tu-darmstadt.de/id/eprint/20386
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