UAV Dynamic Path Planning Based on Obstacle Position Prediction in an Unknown Environment

The application of unmanned aerial vehicle (UAV) has been increasingly popular for its advantages such as convenience and mobility. Thus, its application scenarios have been more and more complex. The UAV must avoid not only stationary obstacles but also dynamic obstacles. Typical UAV path planning...

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Autores principales:	Jianxin Feng, Jingze Zhang, Geng Zhang, Shuang Xie, Yuanming Ding, Zhiguo Liu
Formato:	article
Lenguaje:	EN
Publicado:	IEEE 2021
Materias:	Artificial potential field Markov chain UAV path planning Electrical engineering. Electronics. Nuclear engineering TK1-9971
Acceso en línea:	https://doaj.org/article/a4fbdba7944c4993aa1be9f60ffc22d0
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id	oai:doaj.org-article:a4fbdba7944c4993aa1be9f60ffc22d0
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spelling	oai:doaj.org-article:a4fbdba7944c4993aa1be9f60ffc22d02021-11-25T00:00:24ZUAV Dynamic Path Planning Based on Obstacle Position Prediction in an Unknown Environment2169-353610.1109/ACCESS.2021.3128295https://doaj.org/article/a4fbdba7944c4993aa1be9f60ffc22d02021-01-01T00:00:00Zhttps://ieeexplore.ieee.org/document/9615093/https://doaj.org/toc/2169-3536The application of unmanned aerial vehicle (UAV) has been increasingly popular for its advantages such as convenience and mobility. Thus, its application scenarios have been more and more complex. The UAV must avoid not only stationary obstacles but also dynamic obstacles. Typical UAV path planning algorithms perform well in avoiding static obstacles but poor in dynamic ones. A new dynamic path planning algorithm based on obstacles’ position prediction and modified artificial potential field - HOAP is proposed in this paper. The Markov prediction model is employed to predict the obstacles’ future position with an obstacle grid map. And to resolve the local minima of the typical APF algorithm, a new virtual obstacle method is put forward. What’s more, the attractive force gain coefficient gradient increase method is proposed to solve local oscillation. Simulation results show that the UAV can finally fly a safer path with high accuracy in an unknown environment with static or dynamic obstacles, and avoid local minima or solve local oscillation at the same time.Jianxin FengJingze ZhangGeng ZhangShuang XieYuanming DingZhiguo LiuIEEEarticleArtificial potential fieldMarkov chainUAV path planningElectrical engineering. Electronics. Nuclear engineeringTK1-9971ENIEEE Access, Vol 9, Pp 154679-154691 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Artificial potential field Markov chain UAV path planning Electrical engineering. Electronics. Nuclear engineering TK1-9971
spellingShingle	Artificial potential field Markov chain UAV path planning Electrical engineering. Electronics. Nuclear engineering TK1-9971 Jianxin Feng Jingze Zhang Geng Zhang Shuang Xie Yuanming Ding Zhiguo Liu UAV Dynamic Path Planning Based on Obstacle Position Prediction in an Unknown Environment
description	The application of unmanned aerial vehicle (UAV) has been increasingly popular for its advantages such as convenience and mobility. Thus, its application scenarios have been more and more complex. The UAV must avoid not only stationary obstacles but also dynamic obstacles. Typical UAV path planning algorithms perform well in avoiding static obstacles but poor in dynamic ones. A new dynamic path planning algorithm based on obstacles’ position prediction and modified artificial potential field - HOAP is proposed in this paper. The Markov prediction model is employed to predict the obstacles’ future position with an obstacle grid map. And to resolve the local minima of the typical APF algorithm, a new virtual obstacle method is put forward. What’s more, the attractive force gain coefficient gradient increase method is proposed to solve local oscillation. Simulation results show that the UAV can finally fly a safer path with high accuracy in an unknown environment with static or dynamic obstacles, and avoid local minima or solve local oscillation at the same time.
format	article
author	Jianxin Feng Jingze Zhang Geng Zhang Shuang Xie Yuanming Ding Zhiguo Liu
author_facet	Jianxin Feng Jingze Zhang Geng Zhang Shuang Xie Yuanming Ding Zhiguo Liu
author_sort	Jianxin Feng
title	UAV Dynamic Path Planning Based on Obstacle Position Prediction in an Unknown Environment
title_short	UAV Dynamic Path Planning Based on Obstacle Position Prediction in an Unknown Environment
title_full	UAV Dynamic Path Planning Based on Obstacle Position Prediction in an Unknown Environment
title_fullStr	UAV Dynamic Path Planning Based on Obstacle Position Prediction in an Unknown Environment
title_full_unstemmed	UAV Dynamic Path Planning Based on Obstacle Position Prediction in an Unknown Environment
title_sort	uav dynamic path planning based on obstacle position prediction in an unknown environment
publisher	IEEE
publishDate	2021
url	https://doaj.org/article/a4fbdba7944c4993aa1be9f60ffc22d0
work_keys_str_mv	AT jianxinfeng uavdynamicpathplanningbasedonobstaclepositionpredictioninanunknownenvironment AT jingzezhang uavdynamicpathplanningbasedonobstaclepositionpredictioninanunknownenvironment AT gengzhang uavdynamicpathplanningbasedonobstaclepositionpredictioninanunknownenvironment AT shuangxie uavdynamicpathplanningbasedonobstaclepositionpredictioninanunknownenvironment AT yuanmingding uavdynamicpathplanningbasedonobstaclepositionpredictioninanunknownenvironment AT zhiguoliu uavdynamicpathplanningbasedonobstaclepositionpredictioninanunknownenvironment
_version_	1718414711312089088

UAV Dynamic Path Planning Based on Obstacle Position Prediction in an Unknown Environment

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