New Node Location Setting Using Random Sample Ratio In Occupancy Area On RRT Algorithm

Putra Wisnu Agung Sucipto and Annisa Firasanti and Seta Samsiana and Eki Ahmad Zaki Hamidi New Node Location Setting Using Random Sample Ratio In Occupancy Area On RRT Algorithm. In: 2022 IEEE 8th International Conference on Computing, Engineering and Design (ICCED), 28-29 Juli 2022, Sukabumi.

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Abstract

The Rapidly Random Tree (RRT) algorithm works to grow trees gradually based on a random sampling process. Each growing branch needs to be arranged so that it can reach the target node. This arrangement is done by maintaining the distance between the edge knot and the parent knot so that they are always in the optimal direction and magnitude. This paper offers an alternative approach to adjust the position of the edge vertices in a branch based on the modeling occupancy area that can be occupied by random knots and the distance function which is formulated using the growth ratio of random samples that inhabit the target area and the area outside it. Based on the experimental results, the algorithm has succeeded in making the tree reach min = 0.5 at JI = 265 with PJ = 4.3 from the root node (1.5) and the target node (5.6). While the basic RRT algorithm has not been able to reach the target node at JI = 265 with the remaining distance to be reached is 2.7442 points.

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