Describing correct UAVs cooperation architectures applied on an anti-terrorism scenario

Abstract In this paper, we propose a solution supporting the correct description of UAVs cooperation architectures and their reconfigurations. This solution is based on a graph transformation system that defines rules for changing UAVs cooperation architecture while being in conformance to an architectural style. An architecture instance is considered correct, if its corresponding graph can be generated by a sequence of rules. To be illustrated, our approach is applied on the SuperSenS project where UAVs have to cooperate during search-and-rescue mission. The UAVs system is used for the surveillance of closed military zones aiming at detecting terrorists and tracking them.

[1]  Serge Chaumette,et al.  Dynamicity Aware Graph Relabeling Systems (DA-GRS), A Local Computation based Model to Describe Manet Algorithms , 2005, IASTED PDCS.

[2]  Qun Zong,et al.  Adaptive finite-time reconfiguration control of unmanned aerial vehicles with a moving leader , 2018, Nonlinear Dynamics.

[3]  Antonios Tsourdos,et al.  Modelling and Verification of Multiple UAV Mission Using SMV , 2009, FMA.

[4]  Khalil Drira,et al.  Enhanced graph rewriting systems for complex software domains , 2014, Software & Systems Modeling.

[5]  Bernhard Rinner,et al.  Communication and Coordination for Drone Networks , 2016, ADHOCNETS.

[6]  Jun Yang,et al.  Model of Collaborative UAV Swarm Toward Coordination and Control Mechanisms Study , 2015, ICCS.

[7]  Bernhard Rinner,et al.  An Autonomous Multi-UAV System for Search and Rescue , 2015, DroNet@MobiSys.

[8]  Flávio Oquendo Exogenously Describing Architectural Emergent Behaviors of Systems-of-Systems with SosADL , 2018, 2018 13th Annual Conference on System of Systems Engineering (SoSE).

[9]  Cao Wenjing,et al.  Comparison of multi-UAV cooperation architectures , 2017, 2017 3rd International Conference on Information Management (ICIM).

[10]  David Garlan,et al.  Formal Modeling and Analysis of Software Architecture: Components, Connectors, and Events , 2003, SFM.

[11]  Ahmed T. Hafez,et al.  Formation reconfiguration of cooperative UAVs via Learning Based Model Predictive Control in an obstacle-loaded environment , 2016, 2016 Annual IEEE Systems Conference (SysCon).

[12]  Khalil Drira,et al.  GMTE: A Tool for Graph Transformation and Exact/Inexact Graph Matching , 2013, GbRPR.

[13]  Shamik Sengupta,et al.  Dynamic self-reconfiguration of unmanned aerial vehicles to serve overloaded hotspot cells , 2019, Comput. Electr. Eng..

[14]  Serge Chaumette,et al.  Characterizing Topological Assumptions of Distributed Algorithms in Dynamic Networks , 2009, SIROCCO.

[15]  Tran Hiep Dinh,et al.  Reconfigurable Multi-UAV Formation Using Angle-Encoded PSO , 2019, 2019 IEEE 15th International Conference on Automation Science and Engineering (CASE).

[16]  Serge Chaumette,et al.  Control of a Remote Swarm of Drones/Robots Through a Local (Possibly Model) Swarm: Qualitative and Quantitative Issues , 2017, PE-WASUN '17.