<abstract xmlns="http://www.w3.org/1999/xhtml">
<p>With the advancement of the robotics world, many path-planning algorithms have been proposed. One of the important algorithms is the Rapidly Exploring Random Tree (RRT) but with the drawback of not guaranteeing the optimal path. This paper solves this problem by proposing a Memorized RRT Optimization Algorithm (MRRTO Algorithm) using memory as an optimization step. The algorithm obtains a single path from the start point, and another from the target point to store only the last visited new node. The method for computing the nearest node depends on the position, when a new node is added, the RRT function checks if there is another node closer to the new node rather than that is closer to the goal point. Simulation results with different environments show that the MRRTO outperforms the original RRT Algorithm, graph algorithms, and metaheuristic algorithms in terms of reducing time consumption, path length, and number of nodes used.</p>
</abstract>

With the advancement of the robotics world, many path-planning algorithms have been proposed. One of the important algorithms is the Rapidly Exploring Random Tree (RRT) but with the drawback of not guaranteeing the optimal path. This paper solves this problem by proposing a Memorized RRT Optimization Algorithm (MRRTO Algorithm) using memory as an optimization step. The algorithm obtains a single path from the start point, and another from the target point to store only the last visited new node. The method for computing the nearest node depends on the position, when a new node is added, the RRT function checks if there is another node closer to the new node rather than that is closer to the goal point. Simulation results with different environments show that the MRRTO outperforms the original RRT Algorithm, graph algorithms, and metaheuristic algorithms in terms of reducing time consumption, path length, and number of nodes used.

Memorized Rapidly Exploring Random Tree Optimization (MRRTO): An Enhanced Algorithm for Robot Path Planning

Control and Systems Engineering Department

Cybernetics and Information Technologies

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

With the advancement of the robotics world, many path-planning algorithms have been proposed. One of the important algorithms is the Rapidly Exploring Random...

Memorized Rapidly Exploring Random Tree Optimization (MRRTO): An Enhanced Algorithm for Robot Path Planning

Publié en ligne: 23 mars 2024

Pages: 190 - 204

Reçu: 22 janv. 2024

Accepté: 07 févr. 2024

DOI: https://doi.org/10.2478/cait-2024-0011

Mots clés
Robot path planning, Rapidly exploring random tree, Memorized RRT Optimization Algorithm, Sampling-based method, Metaheuristic Algorithm

© 2024 Dena Kadhim Muhsen et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Memorized Rapidly Exploring Random Tree Optimization (MRRTO): An Enhanced Algorithm for Robot Path Planning

Publié en ligne: 23 mars 2024

Pages: 190 - 204

Reçu: 22 janv. 2024

Accepté: 07 févr. 2024

DOI: https://doi.org/10.2478/cait-2024-0011

Mots clésRobot path planning, Rapidly exploring random tree, Memorized RRT Optimization Algorithm, Sampling-based method, Metaheuristic Algorithm

© 2024 Dena Kadhim Muhsen et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Mots clés
Robot path planning, Rapidly exploring random tree, Memorized RRT Optimization Algorithm, Sampling-based method, Metaheuristic Algorithm