A Health and Usage Monitoring System for ROS-based service robots

This paper presents a multi-core processing solution for ROS-based service robots. The power management together with the control and availability of the processing resources are supervised by a custom-made Power Management Board (PMB) based on a Digital Signal Processor (DSP) micro controller, implementing a Health and Usage Monitoring System (HUMS). The proposed architecture also allows for the PMB to control the most critical robot functions in case of low battery conditions or impossibility of performing energy harvesting, thus extending the lifespan of the robot. All PMB data is recorded on a SD card so as to allow offline analyses of the robotic mission and, thus, support subsequent maintenance activities. Two different implementations of the proposed system have been fielded in two Multi-Robot Systems (MRS) for environmental monitoring, covering aerial, water surface, and wheeled ground vehicles. An additional implementation of the architecture is currently being deployed on an industrial autonomous logistics robot. These three implementations are presented and discussed.

[1]  Jared Jackson Microsoft robotics studio: A technical introduction , 2007, IEEE Robotics & Automation Magazine.

[2]  Sebastian Thrun,et al.  Perspectives on standardization in mobile robot programming: the Carnegie Mellon Navigation (CARMEN) Toolkit , 2003, Proceedings 2003 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2003) (Cat. No.03CH37453).

[3]  Friedrich M. Wahl,et al.  MiRPA: Middleware for robotic and process control applications , 2007 .

[4]  Kikuo Fujimura,et al.  The intelligent ASIMO: system overview and integration , 2002, IEEE/RSJ International Conference on Intelligent Robots and Systems.

[5]  José Barata,et al.  On the Design of a Robotic System Composed of an Unmanned Surface Vehicle and a Piggybacked VTOL , 2014, DoCEIS.

[6]  Rui Kang,et al.  Benefits and Challenges of System Prognostics , 2012, IEEE Transactions on Reliability.

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

[8]  José Barata,et al.  An autonomous surface-aerial marsupial robotic team for riverine environmental monitoring: Benefiting from coordinated aerial, underwater, and surface level perception , 2014, 2014 IEEE International Conference on Robotics and Biomimetics (ROBIO 2014).

[9]  Gaurav S. Sukhatme,et al.  Obstacle detection from overhead imagery using self-supervised learning for Autonomous Surface Vehicles , 2011, 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[10]  José Barata,et al.  A volumetric representation for obstacle detection in vegetated terrain , 2014, 2014 IEEE International Conference on Robotics and Biomimetics (ROBIO 2014).

[11]  Robert O. Ambrose,et al.  Robonaut 2 - The first humanoid robot in space , 2011, 2011 IEEE International Conference on Robotics and Automation.

[12]  Jindong Tan,et al.  RT-ROS: A real-time ROS architecture on multi-core processors , 2016, Future Gener. Comput. Syst..

[13]  Jose Barata,et al.  An open-source watertight unmanned aerial vehicle for water quality monitoring , 2015, OCEANS 2015 - MTS/IEEE Washington.

[14]  Mitsuharu Morisawa,et al.  Humanoid robot HRP-4 - Humanoid robotics platform with lightweight and slim body , 2011, 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[15]  Paulo Rodrigues,et al.  An Aerial-Ground Robotic Team for Systematic Soil and Biota Sampling in Estuarine Mudflats , 2015, ROBOT.

[16]  P. Lall,et al.  Prognostics and Health Management of Electronic Packaging , 2006, IEEE Transactions on Components and Packaging Technologies.

[17]  Matthias Scheutz,et al.  Development environments for autonomous mobile robots: A survey , 2007, Auton. Robots.