TY  - CHAP
A1  - Thoma, Andreas
A1  - Fisher, Alex
A1  - Braun, Carsten
T1  - Improving the px4 avoid algorithm by bio-inspired flight strategies
T2  - DLRK2020 - „Luft- und Raumfahrt – Verantwortung in allen Dimensionen“
Y1  - 2020
N1  - Deutscher Luft- und Raumfahrtkongress 2020, 1. bis 3. September 2020 – Online, „Luft- und Raumfahrt – Verantwortung in allen Dimensionen“
ER  - 
TY  - CHAP
A1  - Thoma, Andreas
A1  - Fisher, Alex
A1  - Bertrand, Olivier
A1  - Braun, Carsten
ED  - Vouloutsi, Vasiliki
ED  - Mura, Anna
ED  - Tauber, Falk
ED  - Speck, Thomas
ED  - Prescott, Tony J.
ED  - Verschure, Paul F. M. J.
T1  - Evaluation of possible flight strategies for close object evasion from bumblebee experiments
T2  - Living Machines 2020: Biomimetic and Biohybrid Systems
KW  - Obstacle avoidance
KW  - Bumblebees
KW  - Flight control
KW  - UAV
KW  - MAV
Y1  - 2020
SN  - 978-3-030-64312-6
U6  - http://dx.doi.org/10.1007/978-3-030-64313-3_34
N1  - 9th International Conference, Living Machines 2020, Freiburg, Germany, July 28–30, 2020, Proceedings
SP  - 354
EP  - 365
PB  - Springer
CY  - Cham
ER  - 
TY  - CHAP
A1  - Thoma, Andreas
A1  - Stiemer, Luc
A1  - Braun, Carsten
A1  - Fisher, Alex
A1  - Gardi, Alessandro G.
T1  - Potential of hybrid neural network local path planner for small UAV in urban environments
T2  - AIAA SCITECH 2023 Forum
N2  - 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.
Y1  - 2023
U6  - http://dx.doi.org/10.2514/6.2023-2359
N1  - AIAA SCITECH 2023 Forum, 23-27 January 2023, National Harbor, MD & Online
PB  - AIAA
ER  -