• 综合
  • 标题
  • 关键词
  • 摘要
  • 学者
  • 期刊-刊名
  • 期刊-ISSN
  • 会议名称
搜索

作者:

Wang, Jingyao (Wang, Jingyao.) | Yu, Naigong (Yu, Naigong.)

收录:

EI Scopus

摘要:

As the basis of robot kinematics, path planning occupies an important position in artificial intelligence and other fields. However, not many researches have focus on the importance of generating waypoints in path planning. In order to accurately find the most efficient moving path, we proposes a path planning method based on Naive Bayes Classifier and CNN to improve A∗ algorithm, which is a search-based algorithm. It first constructs a cost map of the space where the target object is located, and obtains the starting point, ending point with path contour. Next, we obtain the contours of obstacles to calculate the size, use Naive Bayes to realize the mapping, and update the cost map faster instead of the classifers with network structure that need supervised training. After that, we use CNN to find key waypoints, eliminate redundant waypoints and calculate the robot gait. Finally the path planning of the robot is realized. Experiments were carried out in the simulation environment Gazebo and the real space respectively, and the results showed the effectiveness and feasibility of the method. © 2022 Technical Committee on Control Theory, Chinese Association of Automation.

关键词:

Classifiers Motion planning Computer vision Intelligent robots Robot programming

作者机构:

  • [ 1 ] [Wang, Jingyao]Beijing University of Technology, Faculty of Information Technology, Beijing; 100124, China
  • [ 2 ] [Wang, Jingyao]Beijing University of Technology, Beijing Key Lab of the Computational Intelligence and Intelligent System, Beijing; 100124, China
  • [ 3 ] [Yu, Naigong]Beijing University of Technology, Faculty of Information Technology, Beijing; 100124, China
  • [ 4 ] [Yu, Naigong]Beijing University of Technology, Beijing Key Lab of the Computational Intelligence and Intelligent System, Beijing; 100124, China

通讯作者信息:

电子邮件地址:

查看成果更多字段

相关关键词:

相关文章:

来源 :

ISSN: 1934-1768

年份: 2022

卷: 2022-July

页码: 7030-7035

语种: 英文

被引次数:

WoS核心集被引频次:

SCOPUS被引频次:

ESI高被引论文在榜: 0 展开所有

万方被引频次:

中文被引频次:

近30日浏览量: 0

归属院系:

在线人数/总访问数:396/4879142
地址:北京工业大学图书馆(北京市朝阳区平乐园100号 邮编:100124) 联系我们:010-67392185
版权所有:北京工业大学图书馆 站点建设与维护:北京爱琴海乐之技术有限公司