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  - JOUR
A1  - Thomessen, Karolin
A1  - Thoma, Andreas
A1  - Braun, Carsten
T1  - Bio-inspired altitude changing extension to the 3DVFH* local obstacle avoidance algorithm
JF  - CEAS Aeronautical Journal
N2  - Obstacle avoidance is critical for unmanned aerial vehicles (UAVs) operating autonomously. Obstacle avoidance algorithms either rely on global environment data or local sensor data. Local path planners react to unforeseen objects and plan purely on local sensor information. Similarly, animals need to find feasible paths based on local information about their surroundings. Therefore, their behavior is a valuable source of inspiration for path planning. Bumblebees tend to fly vertically over far-away obstacles and horizontally around close ones, implying two zones for different flight strategies depending on the distance to obstacles. This work enhances the local path planner 3DVFH* with this bio-inspired strategy. The algorithm alters the goal-driven function of the 3DVFH* to climb-preferring if obstacles are far away. Prior experiments with bumblebees led to two definitions of flight zone limits depending on the distance to obstacles, leading to two algorithm variants. Both variants reduce the probability of not reaching the goal of a 3DVFH* implementation in Matlab/Simulink. The best variant, 3DVFH*b-b, reduces this probability from 70.7 to 18.6% in city-like worlds using a strong vertical evasion strategy. Energy consumption is higher, and flight paths are longer compared to the algorithm version with pronounced horizontal evasion tendency. A parameter study analyzes the effect of different weighting factors in the cost function. The best parameter combination shows a failure probability of 6.9% in city-like worlds and reduces energy consumption by 28%. Our findings demonstrate the potential of bio-inspired approaches for improving the performance of local path planning algorithms for UAV.
KW  - UAV
KW  - Obstacle avoidance
KW  - Autonomy
KW  - Local path planning
Y1  - 2023
U6  - http://dx.doi.org/10.1007/s13272-023-00691-w
SN  - 1869-5590 (Online)
SN  - 1869-5582 (Print)
N1  - Corresponding author: Karolin Thomessen
PB  - Springer
CY  - Wien
ER  -