It is also applied to planning in discrete spaces or for hybrid systems. In Star Student Project, it is used to analyze genetic network dynamics or protein–ligand interactions. However, when applied to complex problems, the growth of an RRT can become computationally expensive. Some techniques have been proposed to improve the efficiency of RRT by controlling the sampling domain, reducing the complexity of the nearest neighbor search, or using gap reduction techniques.
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