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# Table of Contents
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1. [Communication-Control Distributed Simulator (CUSCUS)](#communication-control-distributed-simulator-cuscus)
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1. [TL;DR](#tldr)
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2. [Standard Introduction](#standard-introduction)
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3. [What is it?](#what-is-it)
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4. [What does it do?](#what-does-it-do)
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1. [Why the term "Distributed"?](#why-the-term-distributed)
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5. [How does it work?](#how-does-it-work)
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1. [Highlights](#highlights)
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6. [Where to find it?](#where-to-find-it)
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<a id="communication-control-distributed-simulator-cuscus"></a>
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# Communication-Control Distributed Simulator (CUSCUS)
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(It is "distributed" for real)
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<a id="tldr"></a>
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## TL;DR
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([for the meaning of "TL;DR"](http://www.urbandictionary.com/define.php?term=tl%3Bdr))
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By connecting two well-known-in-literature simulation suites (FL-AIR for
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**CONTROL** and NS-3 for **NETWORKING**), we are able to perform real-time
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**accurate simulations of the wireless communication among fleets of
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UAVs**, and analyze the impact of the radio propagation phenomena and
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packet level simulation on the fleet mobility algorithms.
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![img](images/cuscus1.png "Cuscus Screenshot")
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<a id="standard-introduction"></a>
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## Standard Introduction
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The current merging of networking and control research fields within the
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scope of robotic applications is creating fascinating research and
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development opportunities.
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However, the tools for a proper and easy management of experiments still
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lag behind.
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![img](images/concorrettivo.gif "Corrective Consensus in NS3")
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Although different solutions have been proposed to simulate and emulate
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control systems and, more specifically, fleets of Unmanned Aerial
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Vehicles (UAV), still they **do not include an efficient and detailed
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network-side simulation**, which is usually available only on dedicated
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software.
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On the other hand, current advancements in network simulations suites
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often **do not present the possibility to include an accurate description
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of controlled systems**.
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In the middle 2010s, **integrated solutions** of networking and control
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for fleets of UAVs **are still lacking**….
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***UNTIL NOW***
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<a id="what-is-it"></a>
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## What is it?
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We fill such gap in the literature by proposing a **novel simulation
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framework** for networked control system, called **CommUnicationS-Control
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distribUted Simulator (CUSCUS)**. Differently from the state of the art,
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CUSCUS allows simulating both the *UAV networking and flight control*,
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via the integration of two existing tools: the **Framework Libre AIR**
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[(FL-AIR)](https://uav.hds.utc.fr/software-flair/) simulator and the
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mainstream network simulator [NS-3](https://www.nsnam.org/) .
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<a id="what-does-it-do"></a>
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## What does it do?
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Using FL-AIR, a real-time and fine-grained simulation of the
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micro-mobility of each UAV can be achieved, including:
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1. the modeling of virtual **sensors/actuators**,
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2. the modeling of the **aerial networking** a packet-level
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3. the modeling of **formation** control algorithms
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4. the modeling of **flight** control algorithms
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5. the PID regulations and the drone stability.
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<a id="why-the-term-distributed"></a>
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### Why the term "Distributed"?
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Levaraging the FLAIR part, it is possible to create UAV applications and
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test them on a simulated control environment before the actual
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deployment, since the same code can also be **plugged in real drones**.
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![img](images/simulator.jpg "Cuscus Screenshot 2")
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In CUSCUS (and FLAIR), each virtual drone application is responsible to
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simulate its flight model and control and then socket-out the data to a
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*rendering/control room* engine.
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**This means that it is always possible to connect *a real drone* to the
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engine and run it alongside its virtual fellows.**
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![img](images/archi_simu.pdf.png "FLAIR Structure")
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To make the simulation even more realistic and make a step towards the
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usage of fleets of UAVs in Smart city scenarios, we just finished
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implementating a **Scenario Module** in both FL-AIR and NS-3.
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Through the Scenario Module, it is possible to model realistic 3D
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environments, importing the scenario maps directly from OpenStreetMaps
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and taking into account the location of buildings and the street
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topology.
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<a id="how-does-it-work"></a>
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## How does it work?
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We have some papers in review, so it is better to keep that for
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ourselves (for the moment, then it will be public domain, maybe…).
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In the meantime, here's a
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**[neat video](https://drive.google.com/open?id=0B-0NRj4-P-qVcUg2YUNlX19jQ0k)** of it:
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[![img](images/videoscreenshot.png)](https://drive.google.com/open?id=0B-0NRj4-P-qVcUg2YUNlX19jQ0k)
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<a id="highlights"></a>
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### Highlights
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- Leader-following scenario
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- Formation Maintenance
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- **WITH SIMULATED BUILDINGS!!!**
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- **TAKEN DIRECTLY FROM OPENSTREETMAPS**
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- **WITH BUILDING COLLISION AVOIDANCE**
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- Done using the virtual sensors
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- The leader has a set of pre-defined way-points
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- The followers use a Particle Swarm Optimization-based (*PSO*)
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algorithm to keep formation
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- The algorithm needs message exchange to work
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- **All the networking is *simulated at packet level* through NS3 in
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realtime**
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<a id="where-to-find-it"></a>
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## Where to find it?
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**Here!**
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<del>Soon to be available on our svn as fast as our papers get through the review process :D.</del>
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