RL and ANN Based Modular Path Planning Controller for Resource-Constrained Robots in the Indoor Complex Dynamic Environment

doi:10.1109/ACCESS.2018.2882875

CORC > 计算技术研究所 > 中国科学院计算技术研究所 > 中国科学院计算技术研究所期刊论文 > 英文

	RL and ANN Based Modular Path Planning Controller for Resource-Constrained Robots in the Indoor Complex Dynamic Environment
	Ullah, Zakir 3,4; Ullah, Waheed 1; Zhang, Libo 2; Zhang, Lei 4; Xu, Zhiwei 3,4
刊名	IEEE ACCESS
	2018
卷号	6 页码:74557-74568
关键词	RL ANN complex dynamic indoor environment modular path planning resource-constrained robots
ISSN号	2169-3536
DOI	10.1109/ACCESS.2018.2882875
英文摘要	Traditional Reinforcement Learning (RL) approaches are designed to work well in static environments. In many real-world scenarios, the environments are complex and dynamic, in which the performance of traditional RL approaches may drastically degrade. One of the factors which results in the dynamicity and complexity of the environment is a change in the position and number of obstacles. This paper presents a path planning approach for autonomous mobile robots in a complex dynamic indoor environment, where the dynamic pattern of obstacles will not drastically affect the performance of RL models. Two independent modules, collision avoidance without considering the goal position and goal-seeking without considering obstacles avoidance, are trained independently using artificial neural networks and RL to obtain their best control policies. Then, a switching function is used to combine the two trained modules for realizing the obstacle avoidance and global path planning in a complex dynamic indoor environment. Furthermore, this control system is designed with a special focus on the computational and memory requirements of resource-constrained robots. The design was tested in a real-world environment on a mini-robot with constrained resources. Along with the static and dynamic obstacles' avoidance, this system has the ability to achieve both static and dynamic targets. This control system can also be used to train a robot in the real world using RL when the robot cannot afford to collide. Robot behavior in the real ground shows a very strong correlation with the simulation results.
资助项目	CAS-TWAS President's Ph.D. Fellowship Programme, University of Chinese Academy of Sciences ; Innovation Project of Institute of Computing Technology, Chinese Academy of Sciences
WOS研究方向	Computer Science ; Engineering ; Telecommunications
语种	英语
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
WOS记录号	WOS:000454388100001
内容类型	期刊论文
源URL	[http://119.78.100.204/handle/2XEOYT63/3482]
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Ullah, Zakir; Xu, Zhiwei
作者单位	1.Univ Peshawar, Shaykh Zayed Islamic Ctr, Peshawar 25120, Pakistan 2.Chinese Acad Sci, Inst Software, Beijing 100049, Peoples R China 3.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 4.Univ Chinese Acad Sci, Inst Comp Technol, State Key Lab Comp Architecture, Beijing 100190, Peoples R China
推荐引用方式 GB/T 7714	Ullah, Zakir,Ullah, Waheed,Zhang, Libo,et al. RL and ANN Based Modular Path Planning Controller for Resource-Constrained Robots in the Indoor Complex Dynamic Environment[J]. IEEE ACCESS,2018,6:74557-74568.
APA	Ullah, Zakir,Ullah, Waheed,Zhang, Libo,Zhang, Lei,&Xu, Zhiwei.(2018).RL and ANN Based Modular Path Planning Controller for Resource-Constrained Robots in the Indoor Complex Dynamic Environment.IEEE ACCESS,6,74557-74568.
MLA	Ullah, Zakir,et al."RL and ANN Based Modular Path Planning Controller for Resource-Constrained Robots in the Indoor Complex Dynamic Environment".IEEE ACCESS 6(2018):74557-74568.