首页    期刊浏览 2024年11月25日 星期一
登录注册

文章基本信息

  • 标题:Cloud based Real-time Multi-robot Collision Avoidance for Swarm Robotics
  • 本地全文:下载
  • 作者:Hengjing He ; Supun Kamburugamuve ; Geoffrey C. Fox
  • 期刊名称:International Journal of Grid and Distributed Computing
  • 印刷版ISSN:2005-4262
  • 出版年度:2016
  • 卷号:9
  • 期号:6
  • 页码:339-358
  • DOI:10.14257/ijgdc.2016.9.6.30
  • 出版社:SERSC
  • 摘要:Nowadays, Cloud Computing has brought many new and efficient approaches for computation-intensive application areas. One typical area is Cloud based real-time device control system, such as the IoT Cloud Platform. This kind of platform shifts computation load from devices to the Cloud and provides powerful processing capabilities to a simple device. In Swarm robotics, robots are supposed to be small, energy efficient and low-cost, but still smart enough to carry out individual and swarm intelligence. These two goals are normally contradictory to each other. Besides, in real world robot control, real-time on-line data processing is required, but most of the current Cloud Robotic Systems are focusing on off-line batch processing. However, Cloud based real-time device control system may provide a way that leads this research area out of its dilemma. This paper explores the availability of Cloud based real-time control of massive complex robots by implementing a relatively complicated but better performed local collision avoidance algorithm. The Cloud based application and corresponding Cloud driver, which connects the robot and the Cloud, are developed and deployed in Cloud environment. Simulation tests are carried out and the results show that, when the number of robots increases, by simply scaling computation resources for the application, the algorithm can still maintain the preset control frequency. Such characteristics verify that the Cloud Computing environment is a new platform for studying massive complex robots in swarm robotics.
  • 关键词:Internet of things; Cloud Computing; swarm robotics; swarm intelligence; ; collision avoidance; real-time stream processing
国家哲学社会科学文献中心版权所有