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Multi-objective 3D Path Planning for UAVs in Large-Scale Urban Scenarios

Hohmann, Nikolas ; Bujny, Mariusz ; Adamy, Jürgen ; Olhofer, Markus (2022)
Multi-objective 3D Path Planning for UAVs in Large-Scale Urban Scenarios.
2022 IEEE Congress on Evolutionary Computation (CEC). Padua, Italy (18.-23.07.2022)
doi: 10.26083/tuprints-00022339
Conference or Workshop Item, Secondary publication, Postprint

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Item Type: Conference or Workshop Item
Type of entry: Secondary publication
Title: Multi-objective 3D Path Planning for UAVs in Large-Scale Urban Scenarios
Language: English
Date: 2022
Place of Publication: Darmstadt
Publisher: IEEE
Book Title: 2022 IEEE Congress on Evolutionary Computation (CEC) : 2022 Conference Proceedings
Collation: 8 Seiten
Event Title: 2022 IEEE Congress on Evolutionary Computation (CEC)
Event Location: Padua, Italy
Event Dates: 18.-23.07.2022
DOI: 10.26083/tuprints-00022339
Corresponding Links:
Origin: Secondary publication service

In the context of real-world path planning applications for Unmanned Aerial Vehicles (UAVs), aspects such as handling of multiple objectives (e.g., minimizing risk, path length, travel time, energy consumption, or noise pollution), generation of smooth trajectories in 3D space, and the ability to deal with urban environments have to be taken into account jointly by an optimization algorithm to provide practically feasible solutions. Since the currently available methods do not allow for that, in this paper, we propose a holistic approach for solving a Multi-Objective Path Planning (MOPP) problem for UAVs in a three-dimensional, large-scale urban environment. For the tackled optimization problem, we propose an energy model and a noise model for a UAV, following a smooth 3D path. We utilize a path representation based on 3D Non-Uniform Rational B-Splines (NURBS). As optimizers, we use a conventional version of an Evolution Strategy (ES), two standard Multi-Objective Evolutionary Algorithms (MOEAs) – NSGA2 and MO-CMA-ES, and a gradient-based L-BFGS-B approach. To guide the optimization, we propose hybrid versions of the mentioned algorithms by applying an advanced initialization scheme that is based on the exact bidirectional Dijkstra algorithm. We compare the different algorithms with and without hybrid initialization in a statistical analysis, which considers the number of function evaluations and quality features of the obtained Pareto fronts indicating convergence and diversity of the solutions. We evaluate the methods on a realistic 3D urban path planning scenario in New York City, based on real-world data exported from OpenStreetMap. The examination’s results indicate that hybrid initialization is the main factor for the efficient identification of near-optimal solutions.

Uncontrolled Keywords: Solid modeling, Surface reconstruction, Three-dimensional displays, Urban areas, Evolutionary computation, Autonomous aerial vehicles, Trajectory, multi-objective optimization, three-dimensional, path planning, hybrid algorithms, evolutionary algorithms, UAV, unmanned aerial vehicle
Status: Postprint
URN: urn:nbn:de:tuda-tuprints-223392
Classification DDC: 000 Generalities, computers, information > 004 Computer science
500 Science and mathematics > 510 Mathematics
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: 16 Sep 2022 12:16
Last Modified: 09 Nov 2022 12:50
URI: https://tuprints.ulb.tu-darmstadt.de/id/eprint/22339
PPN: 500160287
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