Open-source Multi-UAV Simulator for theROS Environment

Ignacio Mas∗†

Sebastian Curi∗

Ricardo Sanchez Pena∗†

∗Centro de Sistemas y ControlInstituto Tecnologico de Buenos Aires (ITBA)

†Consejo Nacional de Investigaciones Cientıficas y Tecnicas (CONICET)Email: iamas@itba.edu.ar

Abstract—This article presents an open-source simula-tor for multiple Ar.Drone quadrotors that runs under theROS operating system. The simulator includes dynamicmodels of the commercially available Parrot Ar.Dronevehicles, and models of their onboard sensors with theaddition of a simulated GPS receiver. The system wasverified by the implementation of a 3-UAV formationcontrol architecture and results for a step response anda trajectory following test are shown. The interface ofthe simulator with the ROS environment is similar tothat of the open-source driver typically used to connectthrough ROS with the real vehicles. This feature allowsfor a seamless transition between simulation, hardware-in-the-loop testing and full hardware experimentation.

I. INTRODUCTION

Advances in different fields of technology openeda wide range of possibilities in the utilization ofunmanned aerial vehicles (UAVs) for activities suchas remote monitoring, border patrol and surveillance,disaster relief, forest fire detection or archeology.

In general, these applications are preformed by asingle vehicle that carries the onboard instrumentsnecessary to execute the mission.

In recent years, there is a growing interest in replac-ing a single complex vehicle with a platoon of smaller,simpler and cheaper UAVs that together can achievethe same goals or even extend the overall capabilitieswhen operating in a cooperative fashion.

In this article, we present an open-source simulatorfor multiple commercially-available Parrot Ar.Dronequadrotors. The simulator runs under the ROS oper-ating system. The simulator includes dynamic modelsof the Ar.Drone vehicles and models of their onboardsensors, together with a simulated GPS receiver. Thesystem is verified by controlling a platoon of threeAr.Drone quadrotors.

II. BACKGROUND

The context of this work is the development of anautonomous aerial platform that can support researchactivities, as well as the demonstrations of applicationsand proof-of-concepts that can be of interest to theindustry in areas ranging from agriculture and oil and

gas exploration to remote sensing or motion picturefilm-making.

In particular, the overall objective of this work isthe creation of an integral multi-UAV testbed, con-sisting of a simulation environment and a hardwaresystem, that can provide a simple and fast developmentplatform to study guidance, navigation and control ofplatoon of autonomous aerial vehicles, with emphasison the formation control methods, the coordinatednavigation and the early demonstration of potentialapplications.

Many institutions around the world have createdtheir own testbeds to support multi-UAV research.Among them we can cite those that use quadrotors inindoor environments , such as the University of Penn-sylvania [1] or MIT [2], or outdoors using helicopters,like the one developed by UC Berkeley [3]. BrighamYoung University and Stanford University use fixed-wind UAVs [4], [5]. The testbed platform developedby ETH de Zurich allows for acrobatic flights in aprotected enclosed environment [6].

In parallel with these developments, in the last fewyears a new software paradigm for robot applicationshas emerged, taking a strong position in the academicworld: the ROS environment. ROS (Robot OperatingSystem) is an open-source meta-operating system forrobots that provides a communication layer and adata and programming structure that simplifies thedevelopment, the collaboration and the exchange ofresearch tools and algorithms [7]. This system is beingwidely adopted by the scientific community, whichallows for an unprecedented direct access to state-of-the-art developments in the robotics field.

This project makes use of the ROS operating sys-tem to integrate in a simple way different vehicles,personal computers and programming languages –suchas C, MATLAB and Python–, and reuse community-developed robot drivers and tools. Additionally, theseamless integration of ROS and the Gazebo Simu-lation environment allows for high fidelity simulationand hardware-in-the-loop testing.

Fig. 1. Front-end of the developed multi Ar.Drone quadrotor simulator with front-camera capability for position estimation. The bottomwindows show the on-board video image generated by each quadrotor.

III. MULTI-UAV SIMULATION ENVIRONMENT

A high-fidelity Ar.Drone quad-rotor simulator withmulti-vehicle capability was developed based on theopen source single-quadrotor ROS package createdunder the Gazebo simulation environment by the Com-puter Vision Group at the Technical University ofMunich (TUM). The original package was extended toadd a functionality that was not previously available.

The simulator includes plugins that describe thedifferent sensors onboard the commercially-availablequadrotor: an inertial navigation system with 3-axesaccelerometers and 3-axes gyroscopes, a front-viewwide-angle camera and a low resolution bottom-viewcamera [8]. Additionally, a GPS receiver was simulatedfor each vehicle to provide a capability that is notpart of the commercial system, but that we are in theprocess of integrating to our quadrotors to have ab-solute position measurements. The interface betweenthe Gazebo simulation and the ROS environment issimilar to that provided by the Ar.Drone ROS drivercalled ardrone_autonomy, which is based on theofficial AR-Drone SDK version 2.0 and supports bothAR-Drone 1.0 and 2.0. This is the driver we currentlyuse in the lab to operate the Ar.Drone quadrotors.This feature allows for a seamless transition betweensimulation and hardware experimentation. The driverardrone_autonomy was developed in the Auton-omy Lab of Simon Fraser University, and is a fork ofAR-Drone Brown driver created by Brown University.These characteristics result in high fidelity INS/GPS

and camera sensor data as well as a realistic dy-namic model of the group of Ar.Drone quadrotors, anda straightforward implementation of hardware-in-the-loop analysis for a quick and smooth transition intofull hardware demonstrations.

Figure 1 shows the front-end of the upgraded simu-lator that allows for multiple quadrotors to fly simulta-neously and interact with each other. Every quadrotorhas a simulated inertial measurement unit (IMU) withspecifications similar to those of the commercial unitsand a simulated GPS receiver with user-defined errorspecifications. The individual quadrotors use thosesensors for the estimation of navigation parameters,implementing sensor-fusion techniques developed in[9]. In particular, gyroscopes provide angular veloc-ity and by integration the attitude is estimated. Aserrors accumulate during integration, gyroscopes bythemselves are not enough for absolute orientationmeasurement. Accelerometers complement this databy comparing the earth gravitational field with thequadrotor reference frame and thus giving an absoluteroll and pitch measurement. The fusion algorithm isgiven by a Madgwick complementary filter [10] whichuses the high-bandwidth of the gyroscope togetherwith the low-bandwidth of the accelerometer to es-timate orientation.

Position estimation of the quadrotor is achieved byfusing dead-reckoning with GPS measurements froma low-cost receiver. Due to the non-linear dynamics,an Extended Kalman Filter [11] is used to fuse these

two measurements.The quadrotor 3-D models were modified with the

addition of a colored marker (magenta hat) that canbe used by the vehicles to visually localize each otherin the formation in order to develop vision-basedintegrated navigation methods of UAV platoons [12].

IV. FORMATION DEFINITION

In order to verify the functionality of the simulator,a multirobot formation is defined for a group of 3aerial robots, each with 4 degrees of freedom (DOF),specifically, (x, y, z, θ). This is applicable to robotssuch as stabilized quadrotors (UAVs).

The formation is defined following the ClusterSpace Control framework [13]. This strategy concep-tualizes the n-robot system as a single entity, a cluster,and desired motions are specified as a function ofcluster attributes, such as position, orientation, andshape. These attributes guide the selection of a set ofindependent system state variables suitable for speci-fication, control, and monitoring. These state variablesform the system’s cluster space. In this particular case,the cluster is defined with the following specifications:

• The position of the cluster (xc, yc, zc) is de-fined by a frame located at the centroid ofthe formation and its orientation is given bythe (rollc, pitchc, yawc) angles –following theZ − Y − X Euler angles convention– as shownin Figures 2 and 3.

• The shape of the formation is defined by 3variables that specify a triangle using 2 sides andone angle. In particular, p specifies the distancebetween robot 1 and robot 2, q specifies thedistance between robot 1 and robot 3, and β isthe angle formed by robots 2-1-3.

• The orientation of the robots with respect to theorientation of the cluster is given by the variablesφi, where i = 1, 2, 3.

Fig. 2. Cluster parameters definition. Overhead view.

Given this definition, a set of forward and inversekinematic equations can be written to relate the robot

Fig. 3. Cluster parameters definition showing rollc and pitchcvariables.

space variables

r = (x1, y1, z1, θ1, x2, y2, z2, θ2, x3, y3, z3, θ3)T , (1)

with the cluster space variables

c = (xc, yc, zc, rollc, pitchc, yawc, φ1, φ2, φ3, p, q, β)T ,

(2)such that ci = fi(r) and ri = gi(c). Furthermore,velocity kinematics can be derived to obtain the clusterjacobian matrix that relates velocities in both spaces,such that c = J(r)r.

V. UAV FORMATION CONTROLLER

Figure 4 shows the architecture of the formationcontroller implementation. The controller operates inthe space of the formation –cluster space–, whichallows for an intuitive specification of the formationtrajectories. Control commands are computed in clus-ter space where trajectories for each of the formationvariables can be followed. The inverse jacobian matrixconverts the resulting cluster space velocity commandsto robot space velocity commands that are then appliedto the vehicles in the system. Robot sensor informationis converted into cluster space through the jacobian andforward kinematic relationships.

Fig. 4. Cluster space controller block diagram . Solid lines indicatesignals and dotted lines indicate parameter passing.

TABLE ISTANDARD DEVIATION OF THE FORMATION PARAMETERS FOR

TEST 1 AFTER STEADY STATE IS REACHED.

Formation parameters’ std errorsxc (m) 0.11yc (m) 0.22zc (m) 0.13

rollc (rad) 0.004pitchc (rad) 0.0025yawc (rad) 0.044p (m) 0. 032q (m) 0.035β (rad) 0.012

VI. RESULTS

The performance of the system is evaluated imple-menting a proportional controller with the architecturepresented in Section V. For these test cases, theaccelerometers std errors are set to 0.35 g and thegyroscopes std errors are set to 0.01 ◦/s. The GPSreceiver is simulated as Gaussian with zero meanand standard deviation of 2.0 m (std.). The onboardcameras were not used during these tests.

The controller, as well as the formation kinematictransformations, were implemented in Python for na-tive execution in the ROS environment.

Two test cases are presented. In the first one, threequadrotors start on the ground and take off at time t =20 s. The cluster controller then regulates the formationto reach a desired final position given by the clusterparameters

cdes = (5,−5, 15, 0, 0, π/2, 0, 0, 0, 4, 4, π/4)T . (3)

The errors of the different cluster parameters areshown in Figure 5. It can be noted the slow dynamicsof the quadrotors altitude as they climb from 0 m to15 m. The standard deviations of the formation param-eters’ error signals once the steady state is reached areshown in Table I.

For the second test case, the formation follows acluster space trajectory. The input to the controller is

0 20 40 60 80 100 120

−5

0

5

10

15

Time (s)

For

mat

ion

para

met

er e

rror

s

xe (m)

ye (m)

ze (m)

rolle (rad)

pitche (rad)

yawe (rad)

pe (m)

qe (m)

betae (rad)

Fig. 5. Formation parameters errors starting on the ground andtaking off at t = 20 s.

a reference trajectory for each formation parameter:

xc cmd(t) = 5 cos (2πft)

yc cmd(t) = −5(1 +1

2sin (4πft))

zc cmd(t) = 15 +5

2(1− cos (3πft))

yawc cmd(t) = 0.1t

pc cmd(t) = 4

qc cmd(t) = 4

βc cmd(t) = π/4

(4)

where f = 0.01Hz is the trajectory frequency. Theremaining variables are regulated at a constant zerovalue.

The results are shown in Figure 6 for the formationposition, in Figure 7 for the formation orientation, andin Figure 8 for the formation shape parameters.

It can be seen that the formation parameters followclosely the desired trajectories. The uncertainty in therobots’ yaw estimation by the sensor fusion algorithm–due to the lack of an absolute measurement of thismagnitude– affects the xc and yc variables, speciallyat low velocities as can be seen in Figure 6 as well asthe shape of the formation as seen in Figure 8.

Next steps include further verification of the soft-ware and setting up a hardware experimental system tocontrast the simulator results with Ar.Drone hardwareexperimental tests.

VII. CONCLUSION

This article presented an open-source simulator formultiple Ar.Drone quadrotors that runs under the ROSoperating system. The simulator includes dynamicmodels of the commercially available vehicles andmodels of their onboard sensors, together with asimulated GPS receiver. The system functionality wasdemonstrated by the implementation of a formationcontrol architecture, and results for a step responseand a trajectory following test were shown.

Fig. 6. Trajectory following results showing the desired andmeasured values of the xc, yc, zc formation variables.

The interface of the simulator with the ROS envi-ronment is similar to that of the open-source drivertypically used to connect through ROS with the realvehicles. This feature allows for a seamless transitionbetween simulation, hardware-in-the-loop testing, andfull hardware experimentation.

This work is a contribution to the overall objectiveof creating an integral multi-UAV testbed, consistingof a simulation environment and a hardware system,that can provide a simple and fast development plat-form to study guidance, navigation and control ofplatoons of autonomous aerial vehicles, with emphasison formation control methods, coordinated navigationand the early demonstration of potential applications.

ACKNOWLEDGMENT

This work has been sponsored through a variety offunding sources to include USAIT Grant W911NF-14-1-0008, and the grants “Iniciacion a la Investigaciony el Desarrollo Tecnologico”, and ITBACyT 2013-17, Instituto Tecnologico de Buenos Aires (ITBA),Argentina.

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Fig. 7. Trajectory following results showing the desired andmeasured values of the rollc, pitchc, yawc formation variables.

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