TY  - JOUR
A1  - Thoma, Andreas
A1  - Thomessen, Karolin
A1  - Gardi, Alessandro
A1  - Fisher, A.
A1  - Braun, Carsten
T1  - Prioritising paths: An improved cost function for local path planning for UAV in medical applications
JF  - The Aeronautical Journal
N2  - Even the shortest flight through unknown, cluttered environments requires reliable local path planning algorithms to avoid unforeseen obstacles. The algorithm must evaluate alternative flight paths and identify the best path if an obstacle blocks its way. Commonly, weighted sums are used here. This work shows that weighted Chebyshev distances and factorial achievement scalarising functions are suitable alternatives to weighted sums if combined with the 3DVFH* local path planning algorithm. Both methods considerably reduce the failure probability of simulated flights in various environments. The standard 3DVFH* uses a weighted sum and has a failure probability of 50% in the test environments. A factorial achievement scalarising function, which minimises the worst combination of two out of four objective functions, reaches a failure probability of 26%; A weighted Chebyshev distance, which optimises the worst objective, has a failure probability of 30%. These results show promise for further enhancements and to support broader applicability.
KW  - Path planning
KW  - Cost function
KW  - Multi-objective optimization
Y1  - 2023
U6  - https://doi.org/10.1017/aer.2023.68
SN  - 0001-9240 (Print)
SN  - 2059-6464 (Online)
IS  - First View
SP  - 1
EP  - 18
PB  - Cambridge University Press
CY  - Cambridge
ER  - 
TY  - JOUR
A1  - Thoma, Andreas
A1  - Gardi, Alessandro
A1  - Fisher, Alex
A1  - Braun, Carsten
T1  - Improving local path planning for UAV flight in challenging environments by refining cost function weights
JF  - CEAS Aeronautical Journal
N2  - Unmanned Aerial Vehicles (UAV) constantly gain in versatility. However, more reliable path planning algorithms are required until full autonomous UAV operation is possible. This work investigates the algorithm 3DVFH* and analyses its dependency on its cost function weights in 2400 environments. The analysis shows that the 3DVFH* can find a suitable path in every environment. However, a particular type of environment requires a specific choice of cost function weights. For minimal failure, probability interdependencies between the weights of the cost function have to be considered. This dependency reduces the number of control parameters and simplifies the usage of the 3DVFH*. Weights for costs associated with vertical evasion (pitch cost) and vicinity to obstacles (obstacle cost) have the highest influence on the failure probability of the local path planner. Environments with mainly very tall buildings (like large American city centres) require a preference for horizontal avoidance manoeuvres (achieved with high pitch cost weights). In contrast, environments with medium-to-low buildings (like European city centres) benefit from vertical avoidance manoeuvres (achieved with low pitch cost weights). The cost of the vicinity to obstacles also plays an essential role and must be chosen adequately for the environment. Choosing these two weights ideal is sufficient to reduce the failure probability below 10%.
KW  - Bio-inspired systems
KW  - Path planning
KW  - Obstacle avoidance
KW  - Unmanned aerial vehicles
Y1  - 2024
U6  - https://doi.org/10.1007/s13272-024-00741-x
SN  - 1869-5590 (eISSN)
SN  - 1869-5582
N1  - Corresponding author: Andreas Thoma
PB  - Springer
CY  - Wien
ER  -