Research on UUV Obstacle Avoiding Method Based on Recurrent Neural Networks

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

doi:10.1155/2019/6320186

Jianya Yuan, Dan Yu + Show 3 more

Open Access

https://doi.org/10.1155/2019/6320186

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Journal: Complexity	Publication Date: Jan 10, 2019
Citations: 10	License type: CC BY 4.0

Affiliation: Harbin Engineering University, Suihua University

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.

Highlights

The online obstacle avoidance planner is one of the important modules of unmanned underwater vehicle (UUV) which reflects its intelligence level, which requires the UUV to plan a collision-free trajectory autonomously when it navigates in long range and unknown environment
We present an online obstacle avoidance planning method for unmanned underwater vehicle (UUV) based on clockwork recurrent neural network (CW-recurrent neural networks (RNNs)) and long short-term memory (LSTM), respectively
This indicates that LSTM is superior to CW-RNN in processing of long-term memory

Summary

Introduction

The online obstacle avoidance planner is one of the important modules of UUV which reflects its intelligence level, which requires the UUV to plan a collision-free trajectory autonomously when it navigates in long range and unknown environment. It is able to process complex sequential information for learning features from long-term input data and has proven to give state-of-the-art performance on many challenging problems involving precipitation nowcasting, predicting water table depth, traffic forecasting, object tracking, punctuation prediction, and so on. Ma et al found this feature is especially desirable for traffic prediction problems, where future traffic condition is commonly relevant to the previous events with long time spans and proposed a LSTM-based traffic flow prediction method to capture nonlinear traffic dynamic in an effective manner [18]. Chherawala et al presented a handwriting recognition model based on LSTM network which automatically learns features from the input image in a supervised fashion [28]. Due to the strong learning ability of RNN, the obstacle avoidance planners are capable for obstacle avoidance in the environments which far much complex than those environments existed in training samples

UUV System Modeling

Simulation Model of Sonar

The Structures of Obstacle Avoidance Planners

Construction of UUV Autonomous Obstacle Avoidance Planning Learning System

Data Processing and Network Training

Results and Analysis

Conclusion