Please use this identifier to cite or link to this item:
Title: TDPP-Net : achieving three-dimensional path planning via a deep neural network architecture
Authors: Wu, Keyu
Mahdi Abolfazli Esfahani
Yuan, Shenghai
Wang, Han
Keywords: Engineering::Electrical and electronic engineering
Issue Date: 2019
Source: Wu, K., Mahdi Abolfazli Esfahani, Yuan, S., & Wang, H. (2019). TDPP-Net : achieving three-dimensional path planning via a deep neural network architecture. Neurocomputing, 357, 151-162. doi:10.1016/j.neucom.2019.05.001
Journal: Neurocomputing
Abstract: Path planning plays a significant role in autonomous navigation for robots in complex environments and hence has been extensively studied for decades. However, the computational time of most existing methods are dependent on the scale and complexity of environment, which leads to the compromise between time efficiency and path quality. To tackle this challenge, deep neural network based (DNN-based) planning methods have been actively explored. However, despite the success of DNN-based 2D planner, 3D path planning, which is a significant primitive for quite a few autonomous robots, is rarely handled by DNNs. In this paper, we propose a novel end-to-end neural network architecture named Three-Dimensional Path Planning Network (TDPP-Net) to realize DNN-based 3D path planning. Embedding the action decomposition and composition concept, our network predicts 3D actions merely through 2D convolutional neural networks (CNNs). Besides, the computational time of TDPP-Net is almost independent of environmental scale and complexity for each action prediction. The experimental results demonstrate that our approach exhibits remarkable performance for planning real-time paths in unseen 3D environments.
ISSN: 0925-2312
DOI: 10.1016/j.neucom.2019.05.001
Rights: © 2019 Elsevier B.V. All rights reserved. This paper was published in Neurocomputing and is made available with permission of Elsevier B.V.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:EEE Journal Articles

Files in This Item:
File Description SizeFormat 
tdpp-net_preprint.pdf3.33 MBAdobe PDFView/Open

Citations 10

Updated on Jan 18, 2023

Web of ScienceTM
Citations 20

Updated on Jan 22, 2023

Page view(s)

Updated on Jan 27, 2023

Download(s) 50

Updated on Jan 27, 2023

Google ScholarTM




Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.