Integrating Heterogeneous Tools for Physical Simulation of multi-Unmanned Aerial Vehicles

This paper presents a multi-layer software architecture to simulate, in a accurate and realistic way, a set of unmanned aerial vehicles (UAVs) operating in a specific mission. A set of tools are employed, each one to simulate a specific part of the overall UAV hardware and software structure: a 3D visualization engine, a physical simulator, the flight stack and a network simulator to handle interactions among UAVs. A software architecture able to orchestrate and coordinate such tools is proposed, based on multiple layers of processes divided into two categories. The described approach is based on a protocol system for exchanging messages to synchronize the various simulation tools. The simulation of the unmanned aerial vehicles can therefore be performed on a single machine or distributed on several machines in order to create a distributed simulation and spread the workload. In this way, it is possible to simulate the behavior of the UAVs and also to reason about the problems due to network communications.

[1]  Craig W. Reynolds Flocks, herds, and schools: a distributed behavioral model , 1998 .

[2]  George F. Riley,et al.  The ns-3 Network Simulator , 2010, Modeling and Tools for Network Simulation.

[3]  João Pedro Hespanha,et al.  Flocking with fixed-wing UAVs for distributed sensing: A stochastic optimal control approach , 2013, 2013 American Control Conference.

[4]  Guowei Cai,et al.  A Survey of Small-Scale Unmanned Aerial Vehicles: Recent Advances and Future Development Trends , 2014 .

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

[6]  Giancarlo Fortino,et al.  A Mission-Oriented Coordination Framework for Teams of Mobile Aerial and Terrestrial Smart Objects , 2016, Mob. Networks Appl..

[7]  J. Gonçalves,et al.  UAV photogrammetry for topographic monitoring of coastal areas , 2015 .

[8]  Min Xia,et al.  Immune network-based swarm intelligence and its application to unmanned aerial vehicle (UAV) swarm coordination , 2014, Neurocomputing.

[9]  Corrado Santoro,et al.  3D Simulation of Unmanned Aerial Vehicles , 2017, WOA.

[10]  Corrado Santoro,et al.  UAV-based Aerial Monitoring: A Performance Evaluation of a Self-Organising Flocking Algorithm , 2015, 2015 10th International Conference on P2P, Parallel, Grid, Cloud and Internet Computing (3PGCIC).

[11]  Andrew Howard,et al.  Design and use paradigms for Gazebo, an open-source multi-robot simulator , 2004, 2004 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (IEEE Cat. No.04CH37566).

[12]  Giancarlo Fortino,et al.  A fault-tolerant self-organizing flocking approach for UAV aerial survey , 2017, J. Netw. Comput. Appl..

[13]  Tamás Vicsek,et al.  Outdoor flocking and formation flight with autonomous aerial robots , 2014, 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[14]  Grzegorz Chmaj,et al.  Distributed Processing Applications for UAV/drones: A Survey , 2014, ICSEng.

[15]  M. Brian Blake,et al.  An Operation-Time Simulation Framework for UAV Swarm Configuration and Mission Planning , 2013, ICCS.

[16]  Tamás Vicsek,et al.  Flocking algorithm for autonomous flying robots , 2013, Bioinspiration & biomimetics.

[17]  Marc Pollefeys,et al.  PX4: A node-based multithreaded open source robotics framework for deeply embedded platforms , 2015, 2015 IEEE International Conference on Robotics and Automation (ICRA).

[18]  Noury Bouraqadi,et al.  Flocking-Based Multi-Robot Exploration , 2009 .