Research on UUV Obstacle Avoiding Method Based on Recurrent Neural Networks

Changjian Lin, Hongjian Wang, Jianya Yuan, Dan Yu and Chengfeng Li

Complexity, 2019, vol. 2019, 1-16

Abstract:

In this paper, we present an online obstacle avoidance planning method for unmanned underwater vehicle (UUV) based on clockwork recurrent neural network (CW-RNN) and long short-term memory (LSTM), respectively. In essence, UUV online obstacle avoidance planning is a spatiotemporal sequence planning problem with the spatiotemporal data sequence of sensors as input and control instruction to motion controller of UUV as output. And recurrent neural networks (RNNs) have proven to give state-of-the-art performance on many sequence labeling and sequence prediction tasks. In order to train the networks, a UUV obstacle avoidance dataset is generated and an offline training and testing is adopted in this paper. Finally, the proposed two types of RNN based online obstacle avoidance planners are compared in path cost, obstacle avoidance planning success rate, training time, time-consumption, learning, and generalization, respectively. And the good performance of the proposed methods is demonstrated with a series of simulation experiments in different environments.

Date: 2019
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
http://downloads.hindawi.com/journals/8503/2019/6320186.pdf (application/pdf)
http://downloads.hindawi.com/journals/8503/2019/6320186.xml (text/xml)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:hin:complx:6320186

DOI: 10.1155/2019/6320186

Access Statistics for this article

More articles in Complexity from Hindawi
Bibliographic data for series maintained by Mohamed Abdelhakeem ().