Bi-level Path Planning Method for Unmanned Vehicle Based on Deep Reinforcement Learning

With the wide application of intelligent unmanned vehicles,intelligent navigation,path planning and obstacle avoidance technology have become important research contents.This paper proposes model-free deep reinforcement learning algorithms DDPG and SAC,which use environmental information to track to...

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Main Author: HUANG Yuzhou, WANG Lisong, QIN Xiaolin
Format: Article
Language:zho
Published: Editorial office of Computer Science 2023-01-01
Series:Jisuanji kexue
Subjects:
Online Access:https://www.jsjkx.com/fileup/1002-137X/PDF/1002-137X-2023-50-1-194.pdf
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author HUANG Yuzhou, WANG Lisong, QIN Xiaolin
author_facet HUANG Yuzhou, WANG Lisong, QIN Xiaolin
author_sort HUANG Yuzhou, WANG Lisong, QIN Xiaolin
collection DOAJ
description With the wide application of intelligent unmanned vehicles,intelligent navigation,path planning and obstacle avoidance technology have become important research contents.This paper proposes model-free deep reinforcement learning algorithms DDPG and SAC,which use environmental information to track to the target point,avoid static and dynamic obstacles,and can be generally suitable for different environments.Through the combination of global planning and local obstacle avoidance,it solves the path planning problem with better globality and robustness,solves the obstacle avoidance problem with better dynamicity and generalization,and shortens the iteration time.In the network training stage,PID,A<sup>*</sup> and other traditional algorithms are combined to improve the convergence speed and stability of the method.Finally,a variety of experimental scenarios such as navigation and obstacle avoidance are designed in the robot operating system ROS and the simulation program gazebo.Simulation results verify the reliability of the proposed approach,which takes the global and dynamic nature of the problem into account and optimizes the generated paths and time efficiency.
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spelling doaj.art-45f4c6077f754ed39e25c601819e8da82023-04-18T02:33:09ZzhoEditorial office of Computer ScienceJisuanji kexue1002-137X2023-01-0150119420410.11896/jsjkx.220500241Bi-level Path Planning Method for Unmanned Vehicle Based on Deep Reinforcement LearningHUANG Yuzhou, WANG Lisong, QIN Xiaolin0College of Computer Science and Technology,Nanjing University of Aeronautics and Astronautics,Nanjing 211106,ChinaWith the wide application of intelligent unmanned vehicles,intelligent navigation,path planning and obstacle avoidance technology have become important research contents.This paper proposes model-free deep reinforcement learning algorithms DDPG and SAC,which use environmental information to track to the target point,avoid static and dynamic obstacles,and can be generally suitable for different environments.Through the combination of global planning and local obstacle avoidance,it solves the path planning problem with better globality and robustness,solves the obstacle avoidance problem with better dynamicity and generalization,and shortens the iteration time.In the network training stage,PID,A<sup>*</sup> and other traditional algorithms are combined to improve the convergence speed and stability of the method.Finally,a variety of experimental scenarios such as navigation and obstacle avoidance are designed in the robot operating system ROS and the simulation program gazebo.Simulation results verify the reliability of the proposed approach,which takes the global and dynamic nature of the problem into account and optimizes the generated paths and time efficiency.https://www.jsjkx.com/fileup/1002-137X/PDF/1002-137X-2023-50-1-194.pdfunmanned vehicle|obstacle avoidance|path planning|deep reinforcement learning
spellingShingle HUANG Yuzhou, WANG Lisong, QIN Xiaolin
Bi-level Path Planning Method for Unmanned Vehicle Based on Deep Reinforcement Learning
Jisuanji kexue
unmanned vehicle|obstacle avoidance|path planning|deep reinforcement learning
title Bi-level Path Planning Method for Unmanned Vehicle Based on Deep Reinforcement Learning
title_full Bi-level Path Planning Method for Unmanned Vehicle Based on Deep Reinforcement Learning
title_fullStr Bi-level Path Planning Method for Unmanned Vehicle Based on Deep Reinforcement Learning
title_full_unstemmed Bi-level Path Planning Method for Unmanned Vehicle Based on Deep Reinforcement Learning
title_short Bi-level Path Planning Method for Unmanned Vehicle Based on Deep Reinforcement Learning
title_sort bi level path planning method for unmanned vehicle based on deep reinforcement learning
topic unmanned vehicle|obstacle avoidance|path planning|deep reinforcement learning
url https://www.jsjkx.com/fileup/1002-137X/PDF/1002-137X-2023-50-1-194.pdf
work_keys_str_mv AT huangyuzhouwanglisongqinxiaolin bilevelpathplanningmethodforunmannedvehiclebasedondeepreinforcementlearning