Abstract— This paper presents the design and control of amultirotor-based aerial manipulator developed for outdooroperation. The multirotor has eight rotors and large payload tointegrate a 7-degrees of freedom manipulator arm with enougharm payload to perform different missions, and to carrysensors and processing hardware needed for outdoorpositioning. The paper focuses on the control design andimplementation aspects. A stable backstepping-based controllerfor the multirotor that uses the coupled full dynamic model isproposed, and an admittance controller for the manipulatorarm is outlined. Several experimental tests with the aerialmanipulator are also presented. In one of the experiments, theperformance of the pitch attitude controller is compared to aPID controller. Other experiments of the arm controllerfollowing an object with the camera are also presented.

Index Terms—Aerial robotics, aerial manipulation,multirotor nonlinear control

I. INTRODUCTION

In the last years, robotic research has placed muchemphasis in the development of autonomous mobile robotsoperating in unstructured and partially known naturalenvironments [1]. Recently aerial manipulation has receivedincreasing attention. The use of aerial mobile manipulatorswould open a range of applications such as the inspection andmaintenance of aerial power lines, the building of platformsfor the evacuation of people in rescue operations or theconstruction in inaccessible sites.

Most of the published work has been devoted to objectgrasping applications. Several quadrotors and helicopterswith gripping mechanisms in the belly have been developed[2]. Teams of these quadrotors have been used forconstruction of cubic structures with magnetic joints [3], andto build architectural structures with bricks [4]. In [5]modeling and control of a manipulating UAV is studied. Alsoan aerial robot with a small arm has been developed forremote inspection by contact of industrial plants [6][7].Autonomous helicopter-based aerial manipulators have alsobeen developed. A small RC helicopter with a gripper hasbeen used to grasp an object on the ground while in flight [2].[8] and [9] also present prototypes of helicopters withattached manipulators.

A problem that arises in aerial manipulation is that thedynamic behavior of the vehicle changes due to the

*All authors are with the Robotics, Vision and Control group (GRVC),University of Seville, Spain. E-mail: guiller@us.es.

modification of the aerial mass distribution and dynamics bygrasping and manipulating objects. The main effects thatappear and make the dynamic behavior of the multirotor witha manipulator different from the standard multirotorconfiguration that is usually considered are the following:

1. Displacement of the center of mass from the verticalaxis at the geometrical center of the multirotor.

2. Variation of mass distribution: the moments of inertiachange significantly when the arm moves.

3. The dynamic reaction forces and torques generated bythe movement of the arm.

These three effects are not usually taken into accountexplicitly, and are left to the integral term in the feedbackcontroller for correction. The effects of the displacement ofthe mass center have been analyzed by some researchers. Forexample, [11] presents stability limits within which thechanging mass parameters of the system will not destabilizequadrotors and helicopters with standard PID controllers. Onthe other hand, [12] studies the effect of grasping objects at apoint displaced from its center of mass and develops acontroller that takes it into account explicitly. In [13], thecenter of mass is also considered, but the variations of themoments of inertia are discarded. The influence of the centerof mass being above or below the equatorial multirotor planeis analyzed in [14]. Adaptive controllers that compensate theunknown displacement of the center of mass have also beenpresented [15][16].

Figure 1. AMUSE octoquad aerial manipulator in flight

Backstepping [17] is a controller design methodologywith guaranteed stability for nonlinear systems which has

Control of a Multirotor Outdoor Aerial Manipulator

G. Heredia, A.E. Jimenez-Cano, I. Sanchez, D. Llorente, V. Vega,J. Braga, J.A. Acosta and A. Ollero*

been applied to VTOL autonomous vehicles [18]. In [19] theauthors presented a Variable Parameter IntegralBasckstepping (VPIB) controller for a quadrotor with amanipulator, which took into account the first two points(variation of center of mass and moments of inertia with themovement of the arm), but did not consider the third one(dynamic reaction torques). In this paper, a controller thatconsider the full dynamic effects and the variation of themass distribution when the arm moves is presented.

When working with quadrotors in most cases theexperiments are performed in indoor testbeds equipped withmulti-camera real time motion capture systems (i.e. Vicon orOptitrack systems), which provide very accurate attitude andposition estimation. Furthermore, small size quadrotors areused in these indoor testbeds, which have a limitedworkspace. Therefore, the available quadrotor payload for themanipulator and the objects is small, limiting the applicationsrange and the manipulation ability.

In this paper the design and development of a largepayload outdoors aerial manipulator is presented. It uses anoctoquad aerial platform with sufficient payload to carry ahigh payload dexterous manipulator arm for realmanipulation applications, in addition to sensors for outdoorpositioning and powerful computers for autonomousoperation.

The work presented here has been done in the frameworkof the ARCAS FP7 European Project [10], which isdeveloping a cooperative free-flying robot system forassembly and structure construction. The ARCAS systemwill use aerial vehicles (helicopters and quadrotors) withmulti-link manipulators for assembly tasks.

The rest of the paper is organized as follows. Section IIpresents the AMUSE aerial manipulation system. Section IIIdescribes the development of the AMUSE controllers andSection IV include experiments to test the validity of theproposed approach.

II. THE AMUSE AERIAL MANIPULATION SYSTEM

Figure 1 shows the general configuration of the octoquadAerial Manipulator developed at University of SEville(AMUSE). It is a multirotor with a multi-link manipulatorarm that has been specially designed to operate in outdoorscenarios.

The AMUSE aerial platform is a multirotor with eightrotors located at the ends of a four-arm planar structure, withtwo rotors positioned coaxially at the end of each arm, whichis usually known as “octoquad” configuration. The reason touse eight rotors is to get enough lift and payload for theapplication. Each pair of coaxial rotors rotates in oppositedirections to compensate the torque at each arm. Theoctoquad has been preferred over the standard octocopterconfiguration (eight rotors distributed in the same plane)because it is more compact, and it is better suited to fly closeto objects or other obstacles.

The AMUSE octoquad aerial platform has been designedto get additional payload so that the sensors and controlcomputer needed to operate outdoors and a multilinkmanipulator arm with enough payload can be mounted. TheAMUSE mounts eight 750 W motors with 16’’ rotors, which

are located at 41 cm of the center on each of the four bars.The AMUSE total payload is 8 kg, which is available forsensors, processing hardware, the manipulator arm and thearm payload.

Figure 2. Two AMUSE prototypes with different arm configurations. Asmall quadrotor which is similar in size to the Pelican quadrotor fromAscending Technologies is included in the photo for size comparison.

Manipulator arms for aerial robots have strong weightlimitations given the limited payload of these aerial vehicles.Usually the main design criteria are to minimize the total armweight, maximize the load to weight ratio and minimize thedisplacement of the center of gravity when the arm jointsmove, to facilitate control of the robot. A lightweight 3-dofarm prototype previously developed was presented in [19].Another arm prototype with 4 dof developed at University ofSeville is shown in Figure 2-left. Figure 1 presents theAMUSE with a 7-dof arm developed by Robai [20]. With 7dof it has better manipulability than the other configurations,and the maximum payload is 1.5 kg, which is well suited fora large set of applications.

The AMUSE control computers and sensors for outdooroperation are shown in Figure 3. The autopilot is amicrocontroller board to which are connected the IMU, abarometer and an ultrasonic sensor. A Novatel Real-Time-Kinematic Differential GPS (RTK-DGPS) is also connectedto the autopilot. This kind of GPS receivers is able to getcentimeter-level accuracy, which is very helpful to achievestable hovering and low speed flight needed for manipulationoperations.

Figure 3. AMUSE control computers and sensors for outdoor operation.

The Autopilot is connected through an Ethernet link tothe High Level Computer (HLC), which is an Intel i7-basedPC. Furthermore, several cameras are also connected to theHLC (see Figure 4). One of the cameras is mounted on theend effector and it is used for visual servoing and relativepositioning of the objects that are being manipulated. The

second camera shown in Figure 4 is attached to the octoquadframe pointing down, and it is used for relative positioningand hover flight stabilization, aiding the RTK-DGPS andcomplementing it when it is not available in high accuracymode. A third camera for positioning can be also mounted onthe frame pointing down on the other side of the frame (notshown in Figure 4). A 6-axis force/torque sensor can beinstalled at the end effector for contact force/torquemeasurement.

Figure 4. AMUSE octoquad with two cameras: one on the end effector forobject recognition and another one mounted on the frame for positioning

The HLC runs 64 bits Ubuntu server with ROS (RobotOperating System [21]). It also has installed the ARUCOlibrary [22], which can detect different markers in real time.Markers can be placed in objects that are being manipulated,or on ground near interest areas for aiding in positioning.Figure 5 shows an example of a bar with a marker at its base(left) and the relative pose recognition performed by ARUCO(right), including relative position and orientation.

Figure 5. Left: structure bar with a marker at its base (indicated in the photowith a yellow arrow). Right: relative pose recognition performed by

ARUCO, including relative position and orientation.

III. AMUSE CONTROLLERS

A. Mathematical modeling

The full dynamic model of a multirotor with a n-linkmanipulator arm is very complex since it includes thecoupled dynamics of the quadrotor aerial vehicle and themanipulator [23]. The mathematical model can be derivedeither using the Lagrange-Euler or the Newton-Eulerformalisms. The equations in compact matrix form can bewritten as:

+̈ߦ(ߦ)ܤ ,ߦ൫ܥ +൯̇ߦ (ߦ)݃ = ݑ + ௫௧ݑ

where ߦ is the generalized state that include the degrees offreedom of the multirotor plus the corresponding to themanipulator arm joints. Vector u encompasses actuator forcesand torques exerted by the octoquad rotors and the servoactuators of the arm joints, and ௫௧ݑ includes external forces

and torques. The mass matrix (ߦ)ܤ includes all the mass andinertia terms, the matrix ,ߦ൫ܥ ൯̇ߦ the centrifugal and Coriolisterms, and the vector (ߦ)݃ the gravity terms. Since the massmatrix is invertible, the following holds:

=̈ߦ ଵି(ߦ)ܤ +ൣݑ −௫௧ݑ ,ߦ൫ܥ −൯̇ߦ ൧(ߦ)݃

For the AMUSE multirotor, the generalized state ߦ is thefollowing:

=ߦ ߮ߠݖݕݔ] ߰ ଵߛ ଷߛଶߛ ହߛସߛ ]ߛߛ

where ߰,߮,ߠ are the multirotor attitude Euler angles and ߛare the joint angles of the 7 degrees-of-freedom manipulator.

B. Octoquad controller

The AMUSE octoquad controller is adapted from theVariable Parameter Integral Backstepping (VPIB) controllerthat was presented in [19], but using the full coupled dynamicmodel outlined above instead of the simplified model usedwhen originally developed. It is a nonlinear controllerobtained through backstepping with an added integral term,with guaranteed stability. The multirotor controller termsdepend on the manipulator arm joints positions andvelocities, and thus the parameters and gains of the controllervary when the arm moves.

Next, the derivation of the pitch attitude controller will bepresented. The controllers for the other angles are built in asimilar way. Consider the tracking error ଵ݁ = ௗߠ − ,ߠ whereௗߠ is the desired pitch angle, and its dynamics:

ௗభ

ௗ௧= ௗ̇ߠ − ߱

Then a virtual control over the angular speed ߱ can beformulated, which is not a control input. Therefore, thedesired angular speed can be defined as follows:

߱ௗ = ଵ݇ ଵ݁ + ௗ̇ߠ + ଵߣ ଵ߯

with ଵ݇and ଵߣ positive constants and ଵ߯ = ∫ ଵ݁( )߬ ఛ݀௧

the

integral of the pitch tracking error. Next, the angular velocitytracking error ଶ݁ and its dynamics can be defined by:

ଶ݁ = ߱ௗ − ߱

ௗమ

ௗ௧= ଵ݇൫̇ߠௗ − ߱൯+ ௗ̈ߠ + ଵߣ ଵ݁ − ̈ߠ

Using (5) and (6) the attitude tracking error dynamicsequation (4) can be rewritten as:

ௗభ

ௗ௧= − ଵ݇ ଵ݁ − ଵߣ ଵ߯ + ଶ݁

Now, ̈ߠ in (6) can be replaced by its expression in thedynamic model (2), and the control input ܷఏ appearsexplicitly. Then, combining the tracking errors of the position

ଵ݁ and the angular speed ଶ݁, and the integral position trackingerror ଵ߯ using the above equations the control input ܷఏ can becalculated as:

ܷఏ = ܻܫ) + )[(1߁ − ଵ݇ଶ + (ଵߣ ଵ݁ + ( ଵ݇ + ଶ݇) ଶ݁ − ଵ݇ߣଵ ଵ߯]

− ,ߦଵ൫ܦ −൯̈ߦ ,ߦଵ൫ܥ −൯̇ߦ (ߦ)ଵܩ

where ଶ݇ is a positive constant which determines theconvergence speed of the angular speed loop, kଵ, λଵ arepositive parameters, and χଵ the integral tracking error.

,ߦଵ൫ܦ ൯includes̈ߦ corresponding mass and inertia terms from

the full dynamic model equations, ,ߦଵ൫ܥ ൯̇ߦ the Coriolis andcentrifugal terms, and (ߦ)ଵܩ the gravity terms. ݕܫ is themoment of inertia about the axis perpendicular to the plane ofeach body (octoquad and arm links), and ܻܫ = ∑ ݕܫ

ଶୀ is the

total moment of inertia.

In practice, the pitch controller terms can be rearranged inthe following way:

ܷఏ = ܭ](ߛ)ܭ ଵ݁ + ܭ ଶ݁ + ூ߯ܭ ଵ]

− ,ߦଵ൫ܦ −൯̈ߦ ,ߦଵ൫ܥ −൯̇ߦ (ߦ)ଵܩ

where ܭ,ܭ ூܭ, are the parameters of a standard PIDcontroller, (ߛ)ܭ is a variable gain that depends on the armjoint angle positions, and ଵܩ,ଵܥ,ଵܦ are the nonlinear termsdescribed above. The first part of the controller can be seenas a gain-scheduled PID with the different positions of thearm, while the nonlinear terms compensate the other effectsdescribed in Section I. In this way, it is easier to tune thecontroller parameters starting from the standard PIDs that areused by many multirotors as base controllers.

The expression of the other attitude controllers (roll andyaw) and the position controllers can be derived in a similarway.

Despite theoretical advantages of including feedbackaccelerations terms according to (9), note that theirintroduction can be a drawback when using numericalalgorithms in a real-time embedded system if they are notproperly processed.

C. Manipulator arm controller

The controller for the manipulator arm joints could bederived in the same manner than the multirotor controllerfrom the dynamic equations of motion, thus obtaining therequired torques u to drive the motors of the arm joints. Thisapproach has been followed in [23][24], which use a torque-based impedance controller for interaction with objects in theenvironment.

However, there are practical limitations in themanipulator arm design that prevent it. As stated above, armsfor aerial manipulators have strong weight restrictions, due tothe payload limitations of the aerial platforms. The prototypearms that have been designed for the AMUSE use standardservos or Robotis Dynamixel servos to drive the joints. Thisis also the case with arms designed for aerial manipulation[26]. Although some of the larger Dynamixel servos have thepossibility of joint torque control, torque sensing has pooraccuracy, making very difficult if not impossible toimplement torque control.

The AMUSE manipulator arm implements an admittancecontroller, which is a position-based Cartesian impedancecontroller. The admittance controller will command a desiredCartesian position Σௗ for the end effector Tool Center Point(TCP):

Σௗ = Σ் + Σ௧

where Σ் is the TCP position defined by the manipulationtask and trajectory interpolation, and Σ௧ is the additionaldisplacement that would get the desired interaction forcesand torques between the end effector and the objects or theenvironment, calculated by the admittance controller. Then,

Σௗ is transformed through the manipulator inversekinematics ଵିܭ and the desired joint position setpoints aretransmitted to the local joint embedded controllers.

For the inverse kinematic a jacobian-based first-orderalgorithm has been used. As it is well-known, this algorithmminimizes the operational space error between the desiredand the actual end-effector position and orientation,overcoming drift problems induced by other differentialkinematics schemes.

Additionally, internal motions have also been generatedthrough the jacobian null space to force a prescribedconfiguration of the manipulator for a given end-effectorposition and orientation. Thus, a strategy to minimize thedistance from mechanical joint limits has been considered.

Finally, to provide a robust behavior crossing through orclose to singular configurations a modified pseudoinversewith a variable damping factor based on gaussian-weightedfunctions of the manipulability measure has been used.

IV. EXPERIMENTS

Several experiments have been done with the AMUSEprototype to test experimentally the controllers of the aerialmanipulator. See the accompanying video for several sampleexperiments.

A set of experiments have been done to test the attitudecontroller in an outdoor scenario. The experiments have beendone in challenging conditions for the attitude controller,since the AMUSE was commanded to hover at a specificaltitude, and then the arm was commanded to make widemovements at high speed, to test the performance of theattitude controller. Figure 6 shows three different instants ofone of the experiments, with the arm in different positions.During the experiments there was lateral wind with frequentwind gusts, which introduced significant perturbations inattitude control.

Figure 6. Flight experiment of AMUSE with the 7-dof manipulator arm:three different instants when moving the arm.

Several arm joints where moving at the same time inthese experiments. Figure 7 shows the evolution of theAMUSE multirotor pitch attitude angle during theexperiments. The X-axis is situated on the plane of the armand the pitch angle is defined as the rotation of the multirotorin this plane around the Y-axis, which is perpendicular to theplane. Thus, the pitch angle is the most influenced by themovement of the arm in these experiments. The dashed linesin Figure 7 represent the movement of one of the arm joints(joint 3, which is moving at 75 degrees/s). Since all the jointsmove at the same time, the dashed line has been included asan indication of the movement of the arm.

The experiment has been performed with the proposedcontroller and with a standard PID controller that does nottake into account the movement of the arm. In the upper plot

of Figure 7 it is shown the evolution of the pitch angle withthe PID controller. When the arm is not moving and centered,the pitch angle presents oscilations of about 5-5.5 degrees ofamplitude, which are due to the atmospheric perturbationsand the action of the controller (this is common to bothcontrollers). But when the arm begins to move, theoscilations with the PID controller grow rapidly to around 12degrees. The lower plot of Figure 7 shows the time evolutionof the AMUSE multirotor with the proposed controller in thesame experiment. It can be seen that when the arm moves,the oscillations increase only slightly to 6-6.5 degrees. Thus,the porposed controller is able to effectively counteract themovement of the arm to a large extent.

Figure 7. Evolution of the AMUSE pitch attitude angle during theexperiments.Upper plot: with a PID standard controller which does not

consider the arm movement and dynamics. Lower plot: with the proposedfull dynamics backstepping controller.

Another set of experiments have been done with theAMUSE manipulator arm following an object that it is goingto grasp. The object has a label on it (see Figure 5), and acamera placed at the belly of the AMUSE and pointing downto the workspace of the arm detects the label as described inSection II, and obtains the relative position of the object withrespect to the end effector of the arm. The arm controller usesthe inverse kinematics to compute the commands that shouldbe sent to the servos of the seven arm joints. Figure 8 showsthe evolution of the joint angles in an experiment. The bluelines are the reference commands computed by the controller,and the green lines are the actual values reached by the joints.It can be seen that when the change of the reference values istoo fast (as for example at = 74 .ݏ in several joints), theservos are not able to follow it and the response lags thereference for some time. Another important point is that thereferences are checked against workspace limits prior tosending them to the servos, so that the arm does not hit theframe or the rotors.

Figure 8. Time evolution of the joint angles of the 7 dof manipulator arm inan experiment (blue – control references, green – actual manipulator joints).

Figure 9. Position and orientation errors of the arm end effector with respectto the references computed by the vision system in the same experiment

than Figure 8.

Figure 9 shows the position and orientation errors of theend effector of the arm with respect to the referencescomputed by the vision system. It can be seen that theposition errors are less than +/- 0.5 cm most of the time,

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except when the references computed by the vision systemmove too fast as can be seen in Figure 8 (this corresponds tothe target object moving away from the arm tip due tooscillations of the AMUSE multirotor). The orientation errorsare also shown in Figure 8.

V. CONCLUSION

Aerial robotics is evolving to include not only systemswith sensing capabilities but also with the possibility to acton the environment, and particularly with manipulationcapabilities. Aerial manipulators based on quadrotors ormultirotors are being increasingly used, but most of them forindoor operation and with very limited payload, thusrestricting applications. This paper has presented thedevelopment of one of the first large payload multirotor-based aerial manipulators for outdoor operation. Thedynamics and mass distribution of the arm when it is movingaffects significantly the attitude stability of the multirotor. Anonlinear controller for the multirotor which takes intoaccount the full dynamics of the arm has been presented. It isable to dampen the oscillations caused by the arm movementto a large extent, compared to a controller that does notconsider the influence of the arm. Furthermore, an admittancecontroller has been proposed for the manipulator arm.Several experiments with the AMUSE multirotor and the armhave also been presented. This is an ongoing work and moreexperiments are being done to test the controllers in differentconditions.

ACKNOWLEDGMENT

This work has been supported by the ARCAS Project,funded by the European Commission under the FP7 ICTProgramme (ICT-2011-287617) and the CLEAR Project(DPI2011-28937-C02-01), funded by the SpanishGovernment.

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