Prioritising paths: An improved cost function for local path planning for UAV in medical applications
Andreas Thoma, Karolin Thomessen, Alessandro Gardi, Alex Fisher, Carsten Braun
Abstract
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.