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Potential of hybrid neural network local path planner for small UAV in urban environments

  • This work proposes a hybrid algorithm combining an Artificial Neural Network (ANN) with a conventional local path planner to navigate UAVs efficiently in various unknown urban environments. The proposed method of a Hybrid Artificial Neural Network Avoidance System is called HANNAS. The ANN analyses a video stream and classifies the current environment. This information about the current Environment is used to set several control parameters of a conventional local path planner, the 3DVFH*. The local path planner then plans the path toward a specific goal point based on distance data from a depth camera. We trained and tested a state-of-the-art image segmentation algorithm, PP-LiteSeg. The proposed HANNAS method reaches a failure probability of 17%, which is less than half the failure probability of the baseline and around half the failure probability of an improved, bio-inspired version of the 3DVFH*. The proposed HANNAS method does not show any disadvantages regarding flight time or flight distance.

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Author:Andreas ThomaORCiD, Luc Stiemer, Carsten BraunORCiD, Alex Fisher, Alessandro G. Gardi
Parent Title (English):AIAA SCITECH 2023 Forum
Document Type:Conference Proceeding
Year of Completion:2023
Date of the Publication (Server):2023/03/07
Length:13 Seiten
AIAA SCITECH 2023 Forum, 23-27 January 2023, National Harbor, MD & Online
Institutes:FH Aachen / Fachbereich Luft- und Raumfahrttechnik
FH Aachen / ECSM European Center for Sustainable Mobility
collections:Verlag / American Institute of Aeronautics and Astronautics (AIAA)