Abstract
As a sampling-based pathfinding algorithm, Rapidly Exploring Random Trees (RRT) has been widely used in motion planning problems due to the ability to find a feasible path quickly. However, the RRT algorithm still has several shortcomings, such as the large variance in the search time, poor performance in narrow channel scenarios, and being far from the optimal path. In this paper, we propose a new RRT-based path find algorithm, Fast-RRT, to find a near-optimal path quickly. The Fast-RRT algorithm consists of two modules, including Improved RRT and Fast-Optimal. The former is aims to quickly and stably find an initial path, and the latter is to merge multiple initial paths to obtain a near-optimal path. Compared with the RRT algorithm, Fast-RRT shows the following improvements: (1) A Fast-Sampling strategy that only samples in the unreached space of the random tree was introduced to improve the search speed and algorithm stability; (2) A Random Steering strategy expansion strategy was proposed to solve the problem of poor performance in narrow channel scenarios; (3) By fusion and adjustment of paths, a near-optimal path can be faster found by Fast-RRT, 20 times faster than the RRT* algorithm. Owing to these merits, our proposed Fast-RRT outperforms RRT and RRT* in both speed and stability during experiments.
Highlights
We present the related backgrounds of this paper
Random Steering randomly chooses the direction to expand when the expansion fails, which solves the problem of poor performance of the Rapidly Exploring Random Trees (RRT) algorithm in narrow channel scenarios
The results show that the average and variance of the search time within the Improved-RRT algorithm are significantly smaller than those of the RRT algorithm
Summary
Motion planning refers to finding a continuous feasible path, which starts in the initial state and ends in the target state. PRM is a multi-query motion planning algorithm, which obtains a graph representing spatial connectivity through random sampling in the state space, and generates a feasible path through graph search. Random Steering randomly chooses the direction to expand when the expansion fails, which solves the problem of poor performance of the RRT algorithm in narrow channel scenarios By introducing these two improvements, the Improved-RRT algorithm can quickly find a feasible solution. Path fusion can fuse multiple initial paths to obtain a better path, while path fine-tuning can quickly adjust the fusion path, which speeds up the search for the optimal path Due to these advantaged characteristics, the search speed of Fast-RRT for finding a near-optimal path is 20 times faster than the RRT*.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
