Fast Motion Planning via Free C-space Estimation Based on Deep Neural Network
Xiang Li, Qixin Cao, Mingjing Sun, Ganggang Yang
Abstract
This paper presents a novel learning-based method for fast motion planning in high-dimensional spaces. A deep neural network is designed to predict the free configuration space rapidly given the environment point cloud. With a generated roadmap as an approximate view of the free C-space, LazyPRM is applied to find and check the path with A* search. Due to the application of LazyPRM, the presented method can preserve probabilistic completeness and asymptotic optimality. The new algorithm is tested on a 3-DOF robot arm and a 6-DOF UR3 robot to plan in randomly generated obstacle environments. Results indicate that compared to planners including PRM, RRT*, RRT-connect and the original LazyPRM, our method is of the lowest time consumption and relatively short path length, showing good performance on both planning speed and path quality.
BibTeX
@inproceedings{iros2019_fastmotionplanni,
title = {Fast Motion Planning via Free C-space Estimation Based on Deep Neural Network},
author = {Xiang Li and Qixin Cao and Mingjing Sun and Ganggang Yang},
booktitle = {IROS 2019},
year = {2019}
}