Narrow passage RRT*: a new variant of RRT*
by Amine Belaid; Boubekeur Mendil; Ali Djenadi
International Journal of Computational Vision and Robotics (IJCVR), Vol. 12, No. 1, 2022

Abstract: Rapidly exploring random tree star (RRT*) has been widely used for optimal path planning for the reason that can solve high degrees of freedom problems. However, this method has many limitations such as slow convergence rate and solving problems with narrow passages. In addition, the collision checking for this method consumes a lot of time in cluttered environments. In this paper, we present a new variant of RRT* named narrow passage RRT* (NP-RRT*), to deal mainly with narrow passage problems and cluttered environments. Our idea is to generate samples near obstacles to explore efficiently complex regions in the configuration space. We have also implemented a path optimisation technique to speed up the convergence rate. In order to reduce the complexity of collision checking, we used a pre-procedure that localises the obstacles before running the planning process. We demonstrate that the complexity of collision checking with our approach does not depend on a number of obstacles. Simulation results, performed in different environments comparing our algorithm with RRT*, alongside statistical analysis, confirm the efficiency of NP-RRT* method.

Online publication date: Tue, 30-Nov-2021

The full text of this article is only available to individual subscribers or to users at subscribing institutions.

 
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.

Pay per view:
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.

Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Computational Vision and Robotics (IJCVR):
Login with your Inderscience username and password:

Username:        Password:          Forgotten your password?

Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.

If you still need assistance, please email subs@inderscience.com