earticle

논문검색

Cloud based Real-time Multi-robot Collision Avoidance for Swarm Robotics

초록

영어

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.

목차

Abstract
 1. Introduction
 2. Local collision avoidance for non-holonomic robots
 3. IoT Cloud Architecture
 4. Implementation of the Collision Avoidance Algorithm
  4.1. Application Overview
  4.2. IoT Cloud Driver for Collision Avoidance
  4.3. Topology Design
 5. Experiment and Results
  5.1. Application Verification Test
  5.2. Performance Test
  5.3. Discussion and Future Work
 6. Conclusion
 Acknowledgements
 References
 9. Appendix

저자정보

  • Hengjing He State Key Lab. of Power System, Dept. of Electrical Engineering, Tsinghua University, Beijing, China
  • Supun Kamburugamuve School of Informatics and Computing and CGL, Indiana University, Bloomington, USA
  • Geoffrey C. Fox School of Informatics and Computing and CGL, Indiana University, Bloomington, USA
  • Wei Zhao State Key Lab. of Power System, Dept. of Electrical Engineering, Tsinghua University, Beijing, China

참고문헌

자료제공 : 네이버학술정보

    함께 이용한 논문

      ※ 원문제공기관과의 협약기간이 종료되어 열람이 제한될 수 있습니다.

      0개의 논문이 장바구니에 담겼습니다.