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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. (Postprint)
In: 2022 IEEE Congress on Evolutionary Computation (CEC) : 2022 Conference Proceedings,
Darmstadt, IEEE, 2022 IEEE Congress on Evolutionary Computation (CEC), Padua, Italy, 18.-23.07.2022, ISBN 978-1-6654-6708-7,
DOI: 10.26083/tuprints-00022339,
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Item Type: Conference or Workshop Item
Origin: Secondary publication service
Status: Postprint
Title: Multi-objective 3D Path Planning for UAVs in Large-Scale Urban Scenarios
Language: English

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.

Book Title: 2022 IEEE Congress on Evolutionary Computation (CEC) : 2022 Conference Proceedings
Place of Publication: Darmstadt
Publisher: IEEE
Collation: 8 Seiten
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
Classification DDC: 000 Allgemeines, Informatik, Informationswissenschaft > 004 Informatik
500 Naturwissenschaften und Mathematik > 510 Mathematik
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 (from 01.08.2022 renamed Control Methods and Intelligent Systems)
Event Title: 2022 IEEE Congress on Evolutionary Computation (CEC)
Event Location: Padua, Italy
Event Dates: 18.-23.07.2022
Date Deposited: 16 Sep 2022 12:16
Last Modified: 09 Nov 2022 12:50
DOI: 10.26083/tuprints-00022339
Corresponding Links:
URN: urn:nbn:de:tuda-tuprints-223392
URI: https://tuprints.ulb.tu-darmstadt.de/id/eprint/22339
PPN: 500160287
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