Analysis of Minimal Clearance and Algorithm Selection Effect on Path Planning for Autonomous Systems
Ronald Ponguillo-Intriago, Payam Khazaelpour, Ignacio Querol Puchal, Silvio Šemanjski, Daniel E. Ochoa, Sidharta Gautama, Ivana Cavar Semanjski · Ghent University Academic Bibliography (Ghent University) · 2021
There are many path planning algorithms in the literature, with different classifications, domains of use, efficiency to find the shortest path or to make a complete coverage of the area to be studied. In the literature, we can also find evaluations of all these algorithms in terms of their performance in the search for the shortest path, execution time and comparisons between them. In this work, twelve algorithms from the literature were studied to analyze their sensibility to the number of obstacles and the clearance value between them. Data analytics methods were used to make a qualitative study of the sensibility of these algorithms to the constraints studied. For investigation of the problem, two metrics were used, the length of the generated path and the number of iterations used to find the solution. The number of iterations here refers to the number of nodes evaluated by the algorithm when searching for the target node. The results are synthesized in two tables that show the sensibility of the algorithms to the change in the constraints studied and the immunity of others, and the correlation among the algorithms, the constraints and the metrics.