@article{ThomaThomessenGardietal.2023,
  author    = {Thoma, Andreas and Thomessen, Karolin and Gardi, Alessandro and Fisher, A. and Braun, Carsten},
  title     = {Prioritising paths: An improved cost function for local path planning for UAV in medical applications},
  series = {The Aeronautical Journal},
  journal   = {The Aeronautical Journal},
  number    = {First View},
  publisher = {Cambridge University Press},
  address   = {Cambridge},
  issn      = {0001-9240 (Print)},
  doi       = {10.1017/aer.2023.68},
  pages     = {1 -- 18},
  year      = {2023},
  abstract  = {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.},
  language  = {en}
}
@article{ThomaGardiFisheretal.2024,
  author    = {Thoma, Andreas and Gardi, Alessandro and Fisher, Alex and Braun, Carsten},
  title     = {Improving local path planning for UAV flight in challenging environments by refining cost function weights},
  series = {CEAS Aeronautical Journal},
  journal   = {CEAS Aeronautical Journal},
  publisher = {Springer},
  address   = {Wien},
  issn      = {1869-5590 (eISSN)},
  doi       = {10.1007/s13272-024-00741-x},
  pages     = {12 Seiten},
  year      = {2024},
  abstract  = {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\%.},
  language  = {en}
}