PG-RRT: A Gaussian Mixture Model Driven, Kinematically Constrained Bi-directional RRT for Robot Path Planning
Paras Sharma, Ankit Gupta, Dibyendu Ghosh, Vinayak Honkote, Ganeshram Nandakumar, Debasish Ghose
Abstract
Path planning and smooth trajectory generation are critical capabilities for efficient navigation of mobile robots operating in challenging and cluttered environments. For real time and autonomous operations of mobile robots, intelligent algorithms, efficient and light-weight compute, and smooth trajectory are key components. In this work, we propose an intelligent, probabilistic Gaussian mixture model driven Bi-RRT (PG-RRT) algorithm which generates nodes in the most probable regions for faster convergence. The proposed algorithm is tested in various simulated environments including highly cluttered obstacles. The experimental results of PG-RRT are compared with state-of-the-art path planning algorithms. The results show significant improvement in the number of iterations (up to 26X) and runtime (up to 17.5X) demonstrating the superiority of the proposed PG-RRT algorithm.
BibTeX
@inproceedings{iros2021_pgrrtagaussianmi,
title = {PG-RRT: A Gaussian Mixture Model Driven, Kinematically Constrained Bi-directional RRT for Robot Path Planning},
author = {Paras Sharma and Ankit Gupta and Dibyendu Ghosh and Vinayak Honkote and Ganeshram Nandakumar and Debasish Ghose},
booktitle = {IROS 2021},
year = {2021}
}