Path Planning Algorithm Using the Hybridization of the Rapidly-Exploring Random Tree and Ant Colony Systems

Muhammad Aria Rajasa Pohan,Sigit Puji Santosa,Arief Syaichu Rohman,Bambang Riyanto Trilaksono

doi:10.1109/access.2021.3127635

Muhammad Aria Rajasa Pohan, Sigit Puji Santosa + Show 2 more

Open Access

https://doi.org/10.1109/access.2021.3127635

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Journal: IEEE Access	Publication Date: Jan 1, 2021
Citations: 13	License type: CC BY 4.0

Affiliation: Bandung Institute of Technology

Abstract

This paper proposes a path planning algorithm using the hybridization of the rapidly-exploring random tree (RRT) and ant colony system (ACS) algorithms. The RRT algorithm can quickly generate paths. However, the resulting path is suboptimal. Meanwhile, the ACS algorithm can generate the optimal path from the suboptimal previous path information. Then, the proposed algorithm will combine the advantages of RRT with the ACS algorithm. Therefore, it can reach the optimal value with a good convergence speed. We call this proposed algorithm the RRT-ACS algorithm. This study developed a new method for hybridizing the RRT and ACS algorithms for path planning problems. This hybridization process is carried out using one of the ACS principles: the pseudorandom proportional rule. The performance of the proposed algorithm with the RRT*, informed RRT*, RRT*-connect, and informed RRT*-connect algorithms is tested with several benchmark cases. The test results from benchmark case tests with known optimal values indicate that the proposed algorithm has succeeded in achieving those optimal values. Furthermore, statistical tests have also been carried out to verify whether there is a significant difference in performance between the RRT-ACS algorithm and the existing algorithms. The test and statistical analysis results show that the RRT-ACS algorithm has good performance and convergence speed. We also discuss the stability, robustness, convergence, and rapidity of the RRT-ACS algorithm. The results indicates that the RRT-ACS algorithm may be used in applications that require fast and optimal path planning algorithms such as robots and autonomous vehicles.

Highlights

The path planning problem is defined as finding a path in the configuration space that starts at the initial configuration and reaches the goal region while satisfying a set of constraints [1]
This paper focuses on developing a path planning algorithm based on the rapidly-exploring random tree (RRT) algorithm to achieve optimal values with good convergence speeds
COMPARISON OF THE CONVERGENCE RATE AND OPTIMALITY To test the proposed algorithm’s convergence speed and optimality performance, the RRT-ant colony system (ACS) algorithm is compared with the RRT*, RRT*-connect, informed RRT*, and informed RRT*-connect algorithms

Summary

INTRODUCTION

The path planning problem is defined as finding a path in the configuration space that starts at the initial configuration and reaches the goal region while satisfying a set of constraints [1]. This paper focuses on developing a path planning algorithm based on the RRT algorithm to achieve optimal values with good convergence speeds. The contribution of this paper is that it proposes a new method for hybridizing the RRT and ACS algorithms for path planning problems. If the exploration process is chosen, the determination of the new node will be based on the RRT algorithm The novelty of this proposed algorithm is that it improves the performance of the RRT algorithm to reach the optimal value with a good convergence speed. This convergence speed can be increased by limiting the search area through the pheromone distribution.

PRELIMINARIES

THE PROPOSED ALGORITHM

10: Until EndCondition

SIMULATIONS SETUP

CONCLUSION