Gain insights into path cost, solving time, and memory allocation, revealing algorithmic behaviors in diverse grid settings.
Authors: Aya Kherrour, Department of Information Engineering and Computer Science University of Trento; Marco Robol, Department of Information Engineering and Computer Science University of Trento; Marco Roveri, Department of Information Engineering and Computer Science University of Trento; Paolo Giorgini, Department of Information Engineering and Computer Science University of Trento.
RTAA* overlap, indicating similar performances. Correspondingly, LPA*, ARA*, and D* also exhibit overlapping lines, where D* Lite being the algorithm with the lowest path cost. In the context of allocated memory , D* lite allocates the least memory for all distances followed by LPA* and then ARA*. The rest of the algorithms allocate almost a similar amount of memory with D* being the worst.
. Regarding the obtained results for the path cost, D* Lite produces the most optimal paths followed by ARA*, D*, and LPA*. In terms of memory allocation , all algorithms tend to allocate the same amount of memory even when new walls are introduced, with only slight increases.
did not show a clear trend in increasing time relative to grid size, indicating that it may be unpredictable and inefficient for this task. Both D* Lite and LPA* exhibited relatively stable performances. D* Lite has a lower solution time across all grid sizes compared to LPA*. However, both algorithm’s standard deviations show some degree of fluctuation.
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