×
验证码:
换一张
Forgotten Password?
Stay signed in
China Science and Technology Network Pass Registration
×
China Science and Technology Network Pass Registration
Log In
Chinese
|
English
中国科学院沈阳自动化研究所机构知识库
Knowledge Management System of Shenyang Institute of Automation, CAS
Log In
Register
ALL
ORCID
Title
Creator
Subject Area
Keyword
Funding Project
Document Type
Source Publication
Indexed By
Publisher
Date Issued
Date Accessioned
MOST Discipline Catalogue
Study Hall
Image search
Paste the image URL
Home
Collections
Authors
DocType
Subjects
K-Map
News
Search in the results
Collection
Digital Fa... [1]
Authors
郑泽宇 [1]
Document Type
Journal ar... [1]
Date Issued
2020 [1]
Language
英语 [1]
Source Publication
IEEE ACCES... [1]
Funding Project
Liaoning P... [1]
Liaoning P... [1]
Liaoning R... [1]
National K... [1]
National N... [1]
National N... [1]
More...
Indexed By
EI [1]
SCI [1]
Funding Organization
Liaoning P... [1]
Liaoning P... [1]
Liaoning R... [1]
National K... [1]
National N... [1]
Natural Sc... [1]
More...
×
Knowledge Map
SIA OpenIR
Start a Submission
Submissions
Unclaimed
Claimed
Attach Fulltext
Bookmarks
QQ
Weibo
Feedback
Browse/Search Results:
1-1 of 1
Help
Filters
Funding Project:Liaoning Province Department of Education Foundation of China[L2019027]
Selected(
0
)
Clear
Items/Page:
5
10
15
20
25
30
35
40
45
50
55
60
65
70
75
80
85
90
95
100
Sort:
Select
Issue Date Ascending
Issue Date Descending
Submit date Ascending
Submit date Descending
Author Ascending
Author Descending
Journal Impact Factor Ascending
Journal Impact Factor Descending
Title Ascending
Title Descending
WOS Cited Times Ascending
WOS Cited Times Descending
Path Planning Method With Improved Artificial Potential Field-A Reinforcement Learning Perspective
期刊论文
IEEE ACCESS, 2020, 卷号: 8, 页码: 135513-135523
Authors:
Yao QF(么庆丰)
;
Zheng ZY(郑泽宇)
;
Qi, Liang
;
Yuan, Haitao
;
Guo, Xiwang
;
Zhao M(赵明)
;
Liu Z(刘智)
;
Yang TJ(杨天吉)
Adobe PDF(1998Kb)
  |  
Favorite
  |  
View/Download:92/13
  |  
Submit date:2020/08/22
Path planning
Learning (artificial intelligence)
Gravity
Potential energy
Mobile agents
Real-time systems
Reinforcement learning
neural network
potential field
path planning