Global dynamic path‐planning algorithm in gravity‐aided inertial navigation system

Abstract Signals from gyros and accelerometers in an inertial navigation system (INS) can be processed to obtain navigation information while its errors accumulate with time. A path with long‐term characteristics is more likely to encounter unpredictable dynamic conditions. To deal with such problem...

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Main Authors: Shengwu Zhao, Lei Shi, Wenzhe Zhang, Zhihong Deng
Format: Article
Language:English
Published: Wiley 2021-10-01
Series:IET Signal Processing
Online Access:https://doi.org/10.1049/sil2.12056
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spelling doaj-c9705846e8cc4af892abc1c2045c72642021-09-14T08:09:28ZengWileyIET Signal Processing1751-96751751-96832021-10-0115851052010.1049/sil2.12056Global dynamic path‐planning algorithm in gravity‐aided inertial navigation systemShengwu Zhao0Lei Shi1Wenzhe Zhang2Zhihong Deng3School of Automation Beijing Institute of Technology Beijing ChinaSchool of Automation Beijing Institute of Technology Beijing ChinaSchool of Automation Beijing Institute of Technology Beijing ChinaSchool of Automation Beijing Institute of Technology Beijing ChinaAbstract Signals from gyros and accelerometers in an inertial navigation system (INS) can be processed to obtain navigation information while its errors accumulate with time. A path with long‐term characteristics is more likely to encounter unpredictable dynamic conditions. To deal with such problems, this work improves the global static path‐planning algorithm based on the A* algorithm, and the local dynamic path‐planning algorithm based on dynamic window approach (DWA). In the global planning algorithm, the gravity index and the heading index are incorporated into the cost function of the A* algorithm to consider the mismatches and the deflection of the path. Furthermore, the search method of the A* algorithm is also improved. In the local planning algorithm, the motion model of the DWA algorithm is improved to a high‐precision model and expressed in a geographic coordinate system. Finally, the improved global static planning and local dynamic planning are combined as the global dynamic path‐planning algorithm of an underwater vehicle. The simulation results show that the global dynamic path‐planning algorithm proposed can realise obstacle‐free navigation of the underwater vehicle.https://doi.org/10.1049/sil2.12056
collection DOAJ
language English
format Article
sources DOAJ
author Shengwu Zhao
Lei Shi
Wenzhe Zhang
Zhihong Deng
spellingShingle Shengwu Zhao
Lei Shi
Wenzhe Zhang
Zhihong Deng
Global dynamic path‐planning algorithm in gravity‐aided inertial navigation system
IET Signal Processing
author_facet Shengwu Zhao
Lei Shi
Wenzhe Zhang
Zhihong Deng
author_sort Shengwu Zhao
title Global dynamic path‐planning algorithm in gravity‐aided inertial navigation system
title_short Global dynamic path‐planning algorithm in gravity‐aided inertial navigation system
title_full Global dynamic path‐planning algorithm in gravity‐aided inertial navigation system
title_fullStr Global dynamic path‐planning algorithm in gravity‐aided inertial navigation system
title_full_unstemmed Global dynamic path‐planning algorithm in gravity‐aided inertial navigation system
title_sort global dynamic path‐planning algorithm in gravity‐aided inertial navigation system
publisher Wiley
series IET Signal Processing
issn 1751-9675
1751-9683
publishDate 2021-10-01
description Abstract Signals from gyros and accelerometers in an inertial navigation system (INS) can be processed to obtain navigation information while its errors accumulate with time. A path with long‐term characteristics is more likely to encounter unpredictable dynamic conditions. To deal with such problems, this work improves the global static path‐planning algorithm based on the A* algorithm, and the local dynamic path‐planning algorithm based on dynamic window approach (DWA). In the global planning algorithm, the gravity index and the heading index are incorporated into the cost function of the A* algorithm to consider the mismatches and the deflection of the path. Furthermore, the search method of the A* algorithm is also improved. In the local planning algorithm, the motion model of the DWA algorithm is improved to a high‐precision model and expressed in a geographic coordinate system. Finally, the improved global static planning and local dynamic planning are combined as the global dynamic path‐planning algorithm of an underwater vehicle. The simulation results show that the global dynamic path‐planning algorithm proposed can realise obstacle‐free navigation of the underwater vehicle.
url https://doi.org/10.1049/sil2.12056
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AT leishi globaldynamicpathplanningalgorithmingravityaidedinertialnavigationsystem
AT wenzhezhang globaldynamicpathplanningalgorithmingravityaidedinertialnavigationsystem
AT zhihongdeng globaldynamicpathplanningalgorithmingravityaidedinertialnavigationsystem
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