Iterative Learning-Based Path and Speed Profile Optimization for an Unmanned Surface Vehicle

Iterative Learning-Based Path and Speed Profile Optimization for an Unmanned Surface Vehicle

Most path-planning algorithms can generate a reasonable path by considering the kinematic characteristics of the vehicles and the obstacles in hydrographic survey activities. However, few studies consider the influence of vehicle dynamics, although excluding system dynamics may considerably damage t...

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Bibliographic Details
Main Authors: Yang Yang, Quan Li, Junnan Zhang, Yangmin Xie
Format: Article
Language:English
Published: MDPI AG 2020-01-01
Series:Sensors
Subjects:
usv
iterative parameter-tuning
path-smoothing
speed profile design
Online Access:https://www.mdpi.com/1424-8220/20/2/439
id doaj-614531b9c78845b69a0d3429a4d53f6d
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spelling doaj-614531b9c78845b69a0d3429a4d53f6d2020-11-25T01:32:46ZengMDPI AGSensors1424-82202020-01-0120243910.3390/s20020439s20020439Iterative Learning-Based Path and Speed Profile Optimization for an Unmanned Surface VehicleYang Yang0Quan Li1Junnan Zhang2Yangmin Xie3Research Institute of USV Engineering, Shanghai University, Shanghai 200444, ChinaResearch Institute of USV Engineering, Shanghai University, Shanghai 200444, ChinaResearch Institute of USV Engineering, Shanghai University, Shanghai 200444, ChinaShanghai Key Laboratory of Intelligent Manufacturing and Robotics, Shanghai University, Shanghai 200444, ChinaMost path-planning algorithms can generate a reasonable path by considering the kinematic characteristics of the vehicles and the obstacles in hydrographic survey activities. However, few studies consider the influence of vehicle dynamics, although excluding system dynamics may considerably damage the measurement accuracy especially when turning at high speed. In this study, an adaptive iterative learning algorithm is proposed to optimize the turning parameters, which accounts for the dynamic characteristics of unmanned surface vehicles (USVs). The resulting optimal turning radius and speed are used to generate the path and speed profiles. The simulation results show that the proposed path-smoothing and speed profile design algorithms can largely increase the path-following performance, which potentially can help to improve the measurement accuracy of various activities.https://www.mdpi.com/1424-8220/20/2/439usviterative parameter-tuningpath-smoothingspeed profile design
collection DOAJ
language English
format Article
sources DOAJ
author Yang Yang
Quan Li
Junnan Zhang
Yangmin Xie
spellingShingle Yang Yang
Quan Li
Junnan Zhang
Yangmin Xie
Iterative Learning-Based Path and Speed Profile Optimization for an Unmanned Surface Vehicle
Sensors
usv
iterative parameter-tuning
path-smoothing
speed profile design
author_facet Yang Yang
Quan Li
Junnan Zhang
Yangmin Xie
author_sort Yang Yang
title Iterative Learning-Based Path and Speed Profile Optimization for an Unmanned Surface Vehicle
title_short Iterative Learning-Based Path and Speed Profile Optimization for an Unmanned Surface Vehicle
title_full Iterative Learning-Based Path and Speed Profile Optimization for an Unmanned Surface Vehicle
title_fullStr Iterative Learning-Based Path and Speed Profile Optimization for an Unmanned Surface Vehicle
title_full_unstemmed Iterative Learning-Based Path and Speed Profile Optimization for an Unmanned Surface Vehicle
title_sort iterative learning-based path and speed profile optimization for an unmanned surface vehicle
publisher MDPI AG
series Sensors
issn 1424-8220
publishDate 2020-01-01
description Most path-planning algorithms can generate a reasonable path by considering the kinematic characteristics of the vehicles and the obstacles in hydrographic survey activities. However, few studies consider the influence of vehicle dynamics, although excluding system dynamics may considerably damage the measurement accuracy especially when turning at high speed. In this study, an adaptive iterative learning algorithm is proposed to optimize the turning parameters, which accounts for the dynamic characteristics of unmanned surface vehicles (USVs). The resulting optimal turning radius and speed are used to generate the path and speed profiles. The simulation results show that the proposed path-smoothing and speed profile design algorithms can largely increase the path-following performance, which potentially can help to improve the measurement accuracy of various activities.
topic usv
iterative parameter-tuning
path-smoothing
speed profile design
url https://www.mdpi.com/1424-8220/20/2/439
work_keys_str_mv AT yangyang iterativelearningbasedpathandspeedprofileoptimizationforanunmannedsurfacevehicle
AT quanli iterativelearningbasedpathandspeedprofileoptimizationforanunmannedsurfacevehicle
AT junnanzhang iterativelearningbasedpathandspeedprofileoptimizationforanunmannedsurfacevehicle
AT yangminxie iterativelearningbasedpathandspeedprofileoptimizationforanunmannedsurfacevehicle
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