The Swarm as a Service: Virtualization of Motion

A defining aspect of Cyber-physical systems are the sensors and actuators of a computing system, which interact with the physical world. While sensors and actuators can take on many different forms, one type of actuator, namely motors, usually has the highest influence on the lifetime due to the high energy consumption. Therefore, it is necessary to reduce physical movement of robots as much as possible without reducing the quality of the application. In this paper we introduce virtual movement of applications, meaning that parts of an application can be executed on arbitrary robots that can reach the locations defined by the application programmer with minimal effort. As we assume that robots will be shared by multiple applications, programming virtual movement within the application is not only cumbersome, but impossible, as the location and status of a robot may be changed by a different application. Therefore, we provide a middleware abstraction which takes care of virtual movement and hides it from the application programmer.

[1]  Fethi Belkhouche,et al.  Reactive Path Planning in a Dynamic Environment , 2009, IEEE Transactions on Robotics.

[2]  Charles W. Warren,et al.  Multiple robot path coordination using artificial potential fields , 1990, Proceedings., IEEE International Conference on Robotics and Automation.

[3]  Christian Laugier,et al.  Path-velocity decomposition revisited and applied to dynamic trajectory planning , 1993, [1993] Proceedings IEEE International Conference on Robotics and Automation.

[4]  Jiangchuan Huang,et al.  From the Real Vehicle to the Virtual Vehicle , 2013 .

[5]  Daniel Graff,et al.  On the Need of Systemic Support for Spatio-Temporal Programming of Mobile Robot Swarms , 2015, 2015 11th International Conference on Mobile Ad-hoc and Sensor Networks (MSN).

[6]  Munther A. Dahleh,et al.  Dynamic Traveling Repairperson Problem for dynamic systems , 2008, 2008 47th IEEE Conference on Decision and Control.

[7]  Alfonso García-Cerezo,et al.  A Mobile Robots Trajectory Planning Approach under Motion Restrictions , 1999, Integr. Comput. Aided Eng..

[8]  S. Zucker,et al.  Toward Efficient Trajectory Planning: The Path-Velocity Decomposition , 1986 .

[9]  Zbigniew Michalewicz,et al.  Path Planning in Dynamic Environments , 2005, Innovations in Robot Mobility and Control.

[10]  Julie A. Shah,et al.  Fast Scheduling of Multi-Robot Teams with Temporospatial Constraints , 2013, Robotics: Science and Systems.

[11]  Rachid Alami,et al.  Multi-robot cooperation through incremental plan-merging , 1995, Proceedings of 1995 IEEE International Conference on Robotics and Automation.

[12]  Daniel Graff,et al.  Cyber-physical systems—exemplary applications and a distributed execution platform , 2016, 2016 IEEE International Conference on Emerging Technologies and Innovative Business Practices for the Transformation of Societies (EmergiTech).

[13]  Steven Okamoto,et al.  Dynamic Multi-Agent Task Allocation with Spatial and Temporal Constraints , 2014, AAAI.

[14]  Emilio Frazzoli,et al.  A Stochastic and Dynamic Vehicle Routing Problem with Time Windows and Customer Impatience , 2009, Mob. Networks Appl..

[15]  Tomás Lozano-Pérez,et al.  On multiple moving objects , 2005, Algorithmica.

[16]  Nasser A. El-Sherbeny,et al.  Vehicle routing with time windows: An overview of exact, heuristic and metaheuristic methods , 2010 .