DewROS: A Platform for Informed Dew Robotics in ROS

In recent years Cloud Robotics technology has been proposed to overcome the constraints imposed by the resources of standalone robots. We can imagine that in near future robots will be very present in everyday life and interact with humans, so it is necessary to guarantee that robots could make decisions even if the connection to the cloud is unavailable. It is then important to move the critical tasks on the edge devices in order to make them always accessible, not following the Cloud Robotics paradigm but the Dew Robotics one instead. In this paper we propose DewROS, a platform for Dew Robotics that uses monitoring entities to monitor the system status in order to adapt the application operating conditions. In particular in this work we describe the DewROS platform and its application in the case of video analysis in a surveillance scenario. The results provided in this paper demonstrate how DewROS allows us to exploit at their best the limited resources of our robots.

[1]  Roland Siegwart,et al.  The SHERPA project: Smart collaboration between humans and ground-aerial robots for improving rescuing activities in alpine environments , 2012, 2012 IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR).

[2]  Antonio Pescapè,et al.  Discovering Topologies at Router Level: Part II , 2007, IEEE GLOBECOM 2007 - IEEE Global Telecommunications Conference.

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

[4]  Javier Ramírez De La Pinta,et al.  Off the Shelf Cloud Robotics for the Smart Home: Empowering a Wireless Robot through Cloud Computing , 2017, Sensors.

[5]  Antonio Pescapè,et al.  Efficient Storage and Processing of High-Volume Network Monitoring Data , 2013, IEEE Transactions on Network and Service Management.

[6]  Prithviraj Dasgupta,et al.  A Comprehensive Survey of Recent Trends in Cloud Robotics Architectures and Applications , 2018, Robotics.

[7]  Bharat K. Bhargava,et al.  A Mobile-Cloud Collaborative Traffic Lights Detector for Blind Navigation , 2010, 2010 Eleventh International Conference on Mobile Data Management.

[8]  Kumar Ayush,et al.  Real time visual SLAM using cloud computing , 2013, 2013 Fourth International Conference on Computing, Communications and Networking Technologies (ICCCNT).

[9]  Dmitry Berenson,et al.  Toward cloud-based grasping with uncertainty in shape: Estimating lower bounds on achieving force closure with zero-slip push grasps , 2012, 2012 IEEE International Conference on Robotics and Automation.

[10]  Antonio Pescapè,et al.  On the performance of the wide-area networks interconnecting public-cloud datacenters around the globe , 2017, Comput. Networks.

[11]  Bindu Sharma,et al.  Cloud Computing and Robotics for Disaster Management , 2016, 2016 7th International Conference on Intelligent Systems, Modelling and Simulation (ISMS).

[12]  Toshiaki Tsuji,et al.  Development of a physical therapy robot for rehabilitation databases , 2012, 2012 12th IEEE International Workshop on Advanced Motion Control (AMC).

[13]  Antonio Pescapè,et al.  A tool for the generation of realistic network workload for emerging networking scenarios , 2012, Comput. Networks.

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

[15]  Alessio Botta,et al.  Cloud, Fog, and Dew Robotics: Architectures for Next Generation Applications , 2019, 2019 7th IEEE International Conference on Mobile Cloud Computing, Services, and Engineering (MobileCloud).