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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.
2021 IEEE Symposium Series on Computational Intelligence (SSCI). Orlando, FL, USA (05.-07.12.2021)
doi: 10.26083/tuprints-00020386
Conference or Workshop Item, Secondary publication, Postprint

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Item Type: Conference or Workshop Item
Type of entry: Secondary publication
Title: Hybrid Evolutionary Approach to Multi-objective Path Planning for UAVs
Language: English
Date: 2 February 2022
Place of Publication: Darmstadt
Year of primary publication: 2021
Publisher: IEEE
Book Title: 2021 Symposium Proceedings
Collation: 8 Seiten
Event Title: 2021 IEEE Symposium Series on Computational Intelligence (SSCI)
Event Location: Orlando, FL, USA
Event Dates: 05.-07.12.2021
DOI: 10.26083/tuprints-00020386
Corresponding Links:
Origin: Secondary publication

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.

Status: Postprint
URN: urn:nbn:de:tuda-tuprints-203866
Classification DDC: 000 Generalities, computers, information > 004 Computer science
500 Science and mathematics > 510 Mathematics
600 Technology, medicine, applied sciences > 600 Technology
600 Technology, medicine, applied sciences > 620 Engineering and machine engineering
Divisions: 18 Department of Electrical Engineering and Information Technology > Institut für Automatisierungstechnik und Mechatronik > Control Methods and Robotics (from 01.08.2022 renamed Control Methods and Intelligent Systems)
Date Deposited: 02 Feb 2022 13:44
Last Modified: 06 Dec 2023 09:37
URI: https://tuprints.ulb.tu-darmstadt.de/id/eprint/20386
PPN: 49145287X
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