Abstract
This paper proposed a triangular inequality-based rewiring method for the rapidly exploring random tree (RRT)-Connect robot path-planning algorithm that guarantees the planning time compared to the RRT algorithm, to bring it closer to the optimum. To check the proposed algorithm’s performance, this paper compared the RRT and RRT-Connect algorithms in various environments through simulation. From these experimental results, the proposed algorithm shows both quicker planning time and shorter path length than the RRT algorithm and shorter path length than the RRT-Connect algorithm with a similar number of samples and planning time.
Highlights
With the recent Fourth Industrial Revolution, interest in mobile robots has increased in various fields such as robotics, smart factories, and autonomous driving [1]
To verify the performance of the proposed triangular inequality-based rapidly exploring random tree (RRT)-Connect algorithm in this paper, the RRT algorithm, the RRT-Connect algorithm, and the proposed algorithm are compared in various environment maps shown in the experimental environment through the simulator
Each algorithm was implemented based on the pseudocode (A1–9) shown Sections 3 and 4 (For the RRT algorithm, refer to the pseudocode (AA1) in Appendix A), and the performance measures used for comparison of various algorithms are Number of samples, Path length, and Planning time
Summary
With the recent Fourth Industrial Revolution, interest in mobile robots has increased in various fields such as robotics, smart factories, and autonomous driving [1]. To overcome the limitation of getting closer to the optimum at the expense of planning time, this paper proposes a triangular inequality-based RRT-Connect algorithm that finds an ancestor node as a via point, where the addition of path length from the start point to the via point and path length from the via point to the newly inserted node is the most optimized, based on the principle of triangular inequality and RRT-Connect. This paper shows that the proposed algorithm has a shorter path length than the RRT and RRT-Connect algorithms without sacrificing other performance measures such as the number of samples or planning time.
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