Assisted link prediction (ALP) protocol in robotic communications

Robotic applications are important in both indoor and outdoor environments. Establishing reliable end-to-end communication among robots in such environments are inevitable. Many real-time challenges in robotic communications are mainly due to the dynamic movement of robots, battery constraints, absence of Global Positioning System (GPS), etc. After recognizing these challenges, building a communication framework among the robots demands the prior knowledge of network connectivity. In this paper, we explore the idea of developing real-time link prediction mechanism between robots by Assisted Link Prediction (ALP) protocol. This is a novel method with intelligent decision process, which can be implemented on the real robots. It resolves many real-time challenges like multiple link ambiguity, accuracy of prediction, improving Packet Reception Ratio (PRR) and reducing energy consumption in-terms of lesser retransmissions. Keeping in view the easy portability of this protocol, we develop the entire software package in Robot Operating System (ROS) framework with the help of Gazebo Simulator and port on the real test-bed using Rpi-3 boards.

[1]  Jyotirmoy Karjee,et al.  Distributed Cooperative Communication and Link Prediction in Cloud Robotics , 2017, 2017 IEEE International Conference on Sensing, Communication and Networking (SECON Workshops).

[2]  Luis Montano,et al.  Enforcing Network Connectivity in Robot Team Missions , 2010, Int. J. Robotics Res..

[3]  Anis Koubaa,et al.  F-LQE: A Fuzzy Link Quality Estimator for Wireless Sensor Networks , 2010, EWSN.

[4]  Dana Marinca,et al.  On-line learning and prediction of link quality in wireless sensor networks , 2014, 2014 IEEE Global Communications Conference.

[5]  Bernhard Plattner,et al.  Link quality prediction in mesh networks , 2008, Comput. Commun..

[6]  Luca Maria Gambardella,et al.  A mobility-controlled link quality learning protocol for multi-robot coordination tasks , 2014, 2014 IEEE International Conference on Robotics and Automation (ICRA).

[7]  Erik Maehle,et al.  Data Flow Analysis in ROS , 2014, ISR 2014.

[8]  Vincent Lenders,et al.  BLITZ: Wireless Link Quality Estimation in the Dark , 2013, EWSN.

[9]  Joseph D. Neff,et al.  Link Quality Estimator for a Mobile Robot , 2012, ICINCO.

[10]  Anis Koubaa,et al.  Radio link quality estimation in wireless sensor networks , 2012, ACM Trans. Sens. Networks.

[11]  Alfred O. Hero,et al.  Using proximity and quantized RSS for sensor localization in wireless networks , 2003, WSNA '03.

[12]  David E. Culler,et al.  Evaluation of Efficient Link Reliability Estimators for Low-Power Wireless Networks , 2004 .

[13]  Morgan Quigley,et al.  ROS: an open-source Robot Operating System , 2009, ICRA 2009.

[14]  Tao Liu,et al.  Data-driven link quality prediction using link features , 2014, TOSN.

[15]  Catherine Rosenberg,et al.  What is the right model for wireless channel interference? , 2006, IEEE Transactions on Wireless Communications.

[16]  Mehrzad Malmirchegini,et al.  On the Spatial Predictability of Communication Channels , 2012, IEEE Transactions on Wireless Communications.

[17]  Aiguo Song,et al.  An empirical analysis for evaluating the link quality of robotic sensor networks , 2009, 2009 International Conference on Wireless Communications & Signal Processing.

[18]  Thomas C. Henderson,et al.  Leveraging RSSI for Robotic Repair of Disconnected Wireless Sensor Networks , 2007, Proceedings 2007 IEEE International Conference on Robotics and Automation.