Cloud-Based Autonomous Indoor Navigation: A Case Study

This study designs, integrates and implements a cloud-enabled autonomous robotic navigation system. The system has the following features: map generation and robot coordination via cloud service and video streaming to allow online monitoring and control in case of emergency. The system has been tested to generate a map for a long corridor using two modes: manual and autonomous. The autonomous mode has shown more accurate map. In addition, the field experiments confirm the benefit of offloading the heavy computation to the cloud by significantly shortening the time required to build the map.

[1]  Ole Ravn,et al.  Traversable terrain classification for outdoor autonomous robots using single 2D laser scans , 2006, Integr. Comput. Aided Eng..

[2]  Olivier Simonin,et al.  MinPos : A Novel Frontier Allocation Algorithm for Multi-robot Exploration , 2012, ICIRA.

[3]  Habib Youssef,et al.  ROS Web Services: A Tutorial , 2016 .

[4]  David Portugal,et al.  An evaluation of 2D SLAM techniques available in Robot Operating System , 2013, 2013 IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR).

[5]  Paolo Dario,et al.  Use Case Evaluation of a Cloud Robotics Teleoperation System (Short Paper) , 2016, 2016 5th IEEE International Conference on Cloud Networking (Cloudnet).

[6]  P. Djurić,et al.  Particle filtering , 2003, IEEE Signal Process. Mag..

[7]  Sebastian Zug,et al.  Are laser scanners replaceable by Kinect sensors in robotic applications? , 2012, 2012 IEEE International Symposium on Robotic and Sensors Environments Proceedings.

[8]  Guoqiang Hu,et al.  Cloud robotics: architecture, challenges and applications , 2012, IEEE Network.

[9]  Anis Koubaa,et al.  DroneTrack: Cloud-Based Real-Time Object Tracking Using Unmanned Aerial Vehicles Over the Internet , 2018, IEEE Access.

[10]  Partha Pratim Ray,et al.  Internet of Robotic Things: Concept, Technologies, and Challenges , 2016, IEEE Access.

[11]  Nicholas G. Polson,et al.  Particle Filtering , 2006 .

[12]  Guilherme S. Bastos,et al.  ROSRemote, using ROS on cloud to access robots remotely , 2017, 2017 18th International Conference on Advanced Robotics (ICAR).

[13]  Georgia Sakellari,et al.  Cloud-Based Cyber-Physical Intrusion Detection for Vehicles Using Deep Learning , 2018, IEEE Access.

[14]  Pieter Abbeel,et al.  Image Object Label 3 D CAD Model Candidate Grasps Google Object Recognition Engine Google Cloud Storage Select Feasible Grasp with Highest Success Probability Pose EstimationCamera Robots Cloud 3 D Sensor , 2014 .

[15]  Erik Blasch,et al.  A Holistic Cloud-Enabled Robotics System for Real-Time Video Tracking Application , 2014 .

[16]  Maria Teresa Lazaro,et al.  Multi-robot SLAM using condensed measurements , 2013, 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[17]  Athanasios V. Vasilakos,et al.  Cloud robotics: Current status and open issues , 2016, IEEE Access.

[18]  Anis Koubaa,et al.  ROSLink: Bridging ROS with the Internet-of-Things for Cloud Robotics , 2017 .

[19]  Swagat Kumar,et al.  Managing a fleet of autonomous mobile robots (AMR) using cloud robotics platform , 2017, 2017 European Conference on Mobile Robots (ECMR).

[20]  Stefano Chessa,et al.  Internet of Robotic Things-Converging Sensing / Actuating , Hypoconnectivity , Artificial Intelligence and IoT Platforms , 2017 .

[21]  Raffaello D'Andrea,et al.  Rapyuta: A Cloud Robotics Platform , 2015, IEEE Transactions on Automation Science and Engineering.

[22]  Alessandro Saffiotti,et al.  IoT European Large-Scale Pilots – Integration, Experimentation and Testing , 2017 .

[23]  Sebastien Glaser,et al.  Simultaneous Localization and Mapping: A Survey of Current Trends in Autonomous Driving , 2017, IEEE Transactions on Intelligent Vehicles.

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