Informed-RRT* Using Hybrid Sampling to Finding Fast Final Path Solution
Informed Rapidly-exploring Random Tree* (Informed-RRT*) is the result of the development of the RRT algorithm which can produce an optimal asymptotic path solution but the computation time required is longer. Initially, the Informed-RRT* algorithm was still using the random sampling method, where this method will take a random sample in the search space. This random sampling will make the computation time not optimal. This study aims to design the Informed-RRT* algorithm using a hybrid sampling method. The hybrid sampling method is an integration of several sampling methods. In this test, the performance of the random sampling method will be compared with the performance of the hybrid sampling method in terms of computation time. The test of the hybrid sampling method on the Informed-RRT* algorithm is based on simulation and is carried out in a narrow, clutter, trap environment. The results obtained from this test are that the use of the hybrid sampling method on the Informed-RRT* algorithm is able to produce a faster average computation time performance of 26.4 seconds when compared to the random sampling method in a cluttered environment. In a narrow environment, the hybrid sampling method produces a computation time of 24.52 seconds faster than the random sampling method. In the trap environment, the hybrid sampling method produces a computation time of 5.25 seconds faster than the random sampling method. From the test data, this hybrid sampling method can be an alternative sampling method to be used in the Informed-RRT* algorithm.