Video Transmission Control Under Limited Network Bandwidth for Robot Teleoperation

Abstract

Abstract:

Abstract： This paper presented a bandwidth allocation algorithm for video transmission in robot remote control system. There are multiple video sensors equipped on the robot. The transmission weight of each video was calculated according to the current tasks being executed by the robot. In multiobjective optimization, the algorithm distributeed the transmission bandwidth fairly to each video sensor based on their weight, and could guarantee that the total transmission bandwidth never exceeds the transmission bandwidth provided by the network. The transmission bandwidth was controlled by modifying the resolution and frame rate of the video acquisition. The algorithm reduced the lose rate of frames caused by network congestion, thus protects the integrity of realtime video, makes the teleoperation tasks of the system complete which depend on the visual image quality. The algorithm is tested by inspection tasks in an actual power plant environment. Smooth video transmission service has been provided and the practicability of the algorithm is also verified.

Key words: streaming media transmission, bandwidth allocation, teleoperation robot

CLC Number:

TP 249

LI Liang,WANG Hesheng,CHEN Weidong. Video Transmission Control Under Limited Network Bandwidth for Robot Teleoperation[J]. Journal of Shanghai Jiaotong University, 2014, 48(12): 1700-1707.

References

［1］Braden R, Zhang L, Berson S. Resource reservation protocol (RSVP) ［EP/OL］. (199709) ［20140220］. http:∥www.hjp.at/doc/rfc/rfc2205.html.

［2］Pan P, Schulzrinne H. A simple reservation mechanism for the internet［J］.ACM SIGCOMM Computer Communication Review, 1999, 29(2):89101.

［3］Asokan R, Natarajan A M.An approach for reducing the endtoend delay and increasing network lifetime in mobile adhoc networks［J］. International Journal of Information Technology, 2008, 2(1): 1226.

［4］Kunniyur S, Srikant R. Endtoend congestion control schemes: Utility functions, random losses and ECN Marks［J］. IEEE/ACM Transactions on Networking,200310 11(5): 689702.

［5］Paskalis S, Merakos L, Kaloxylos A. A survey of internet QoS signaling［J］. IEEE Communications Surveys & Tutorials, 2004, 6(4): 3243.

［6］Katabi D, Handley M, Rohrs C. Congestion control for high bandwidthdelay product networks［J］.ACM SIGCOMM Computer Communication Review, 2002, 32(4):89102.

［7］Wang Xiaokai, Du Wenzhuo, Zheng Dongmei. A class of the wireless sensor networks QoS description and evaluation［C］∥Third International Conference on Measuring Technology and Mechatronics Automation (ICMTMA). Shanghai, China: IEEE, 2011: 1619.

［8］李方敏，李恒，刘新华.无线多媒体传感器网络体系结构及QoS保障机制［J］. 计算机科学, 2009, 36(6): 1925.

LI Fangmin, LI Heng, LIU Xinhua. Survey on architecture and QoS for wireless multimedia sensor networks［J］.Computer Science, 2009,36(6):1925.

［9］Rafael C G, Richard E W. Digital image processing［M］. 3rd ed. Beijing: Electronic Industry Press, 2011: 4445.

［10］Low S H, Lapsley D E. Optimization flow controlI: Basic algorithm and convergence［J］. IEEE/ACM Transactions on Networking,1999, 7(6): 861874.

［11］Sawaragi Y. Nakayama H, Tanino T. Theory of multiobjective optimization［M］. 1st ed. Orlando, FL: Academic Press Inc, 1985.