Energy distribution in Dual-UAV collaborative
transportation through load sharing
UAE, Abu Dhabi
Khalifa University of Science and Technology,
P.O. Box 127788, Abu Dhabi, UAE
Faculty of Science,
Engineering and Computing
Kingston University London
London SW15 3DW, UK
Algorythma’s Autonomous Aerial Lab
UAE, Abu Dhabi
School of Engineering Technology ,
401 N Grant Street, West Lafayette, IN 47907, USA
In this paper, a novel dual-UAV collaborative aerial transport strategy based on energy distribution and load
sharing is proposed. This paper presents the ﬁrst experimental demonstration of dual-UAV collaborative aerial
transport while distributing power consumption. The demonstration is performed while distributing the power
consumption between two drones sharing a load based on their battery state of charge. A numerical model of the
dual-hex-rotor-payload is used to validate the proposed strategy. Numerical and hardware tests were conducted
to demonstrate the load distribution using multiple UAV with certain spatial conﬁgurations. Finally, collaborative
aerial transport test scenarios are performed numerically and experimentally. The simulation and experimental
results show the effectiveness and applicability of the proposed strategy.
Aerial transportation of payloads via Unmanned Aerial Vehicles (UAVs) is not a new concept . A commercial online
shop and delivery company 1is currently evaluating the possible usage of aerial vehicles to deliver goods. A recent study
showed that UAV delivery might even help in the reduction of green house emissions caused by freight industry . The
limitation of payload carrying capability of a single UAV can be offset by the use of multiple UAVs.
Various multi-UAV collaborative aerial transport work is present in literature providing several demonstrations of col-
laborative transport as discussed in [3–6]. These studies are focused on the collaboration of multiple UAVs, by either using
co-ordinated motion strategies [5–7] or the leader follower approaches [8, 9]. Path planning and co-operative localization
while transporting a payload using multiple UAVs were presented in [10, 11]. In [12, 13], haptic feedback based tele-
manipulation were proposed, the UAVs were equipped with 1DoF arm that could apply one point contact force to lift an
∗Corresponding author: Mohiuddin, JMR-19-1378
object. The studies mentioned above focus on the collaborative aerial manipulation without taking orientation of the payload
into consideration and its effect on energy distribution between UAVs.
Several papers tackled the challenge of adjusting position and orientations of the payload in mid air [14–18], however,
the uneven thrust requirements and the corresponding power distribution were not considered. In general, the orientation of
the payload and the trajectory depends on the position control of the UAVs. The work presented in  presented a leader
follower approach to achieve the desired pose without communication between UAVs by using non-zero internal force. In 
modeling of re-conﬁgurable cable-driven parallel robots (RCDPR) was used to ﬁnd the relationships between the motion of
quadrotors and the motion of a payload. These studies are not focused on the effects of the payload shape on the energy
distribution during collaborative transport [14–18].
Researchers have focused on collaborative transport of payloads with various shapes such as point mass, rectangular
blocks and deformable linear objects , . A point mass payload requires separation between the UAVs using longer
cables as a result, the UAVs experience a pull towards each other . An increase in the volume of the payload can relieve
the necessity of that pulling force. However, an increase in the volume of the payload increases the implication of the center
of gravity of the payload while distributing the thrust requirements. Most of the studies [5, 8, 9, 11] for collaborative transport
use objects of smaller width and larger length (i.e., higher aspect ratios). In case of lower aspect ratio payload, any difference
in altitude of the collaborating UAVs leads to an uneven thrust requirement, thus causing uneven energy distribution.
The thrust distribution during collaborative transport is discussed in [20,22]. The mechanism proposed by  regulates
the thrust requirements for transporting a point mass. This regulation, however, should be performed taking the energy
availability of the UAVs into consideration. The work presented in  discussed the need of equal load distribution while
transporting deformable linear objects (DLOs), the conﬁguration of multiple UAVs which can provide equal-load distribution
was estimated using particle swarm optimization (PSO). In that case, however, it is assumed that all UAVs are identical and
their battery capacities are also equivalent. The methods presented in [20, 22] were not experimentally tested for veriﬁcation.
The studies [20, 22] were also not focused on the rectangular shaped payload for thrust distribution. This study therefore
focused on the rectangular shaped payload to distribute the thrust during collaborative transport to distribute the energy
Energy distribution management in multi-UAV collaborative transport is important since multi-rotors UAVs are known
for low ﬂight endurance  and power failure in one of the UAVs can make the transportation operation fail. This paper
therefore proposes a novel experimentally veriﬁed strategy to distribute the lifting load of the jointly carried object by
changing the orientation of the payload. Distribution of lifting force also enables us to regulate the power distribution
between the UAVs. This distribution of power consumption can ensure mission completion when one UAV has less state of
charge than the other.
The paper is structured as follows, Section 2 describes the generalized energy aware collaboration strategy for arbitrary
shaped paylaod carried by nUAVs. Section 2 also includes the assumptions used in this study, the multi-UAV-payload model
used for the assesment and validation of the proposed strategy, the power consumption model used. Section 3 is focused
on the application of energy distribution strategy for a 2D object along with the load sharing strategy, constraints and the
description of dual UAV collaborative transport method. Section 4 shows the simulated results while Section 5 shows the
experimental method and results.
2 General energy aware collaboration strategy
A general strategy for load distribution is devised for n=2,3 number of drones, and an arbitrary voluminous payload.
It is not possible to ﬁnd a closed form solution for load distribution when attitude angles of the payload are given. However,
assuming the payload geometry is known, it is possible to iteratively ﬁnd the required payload orientation to satisfy the
load distribution requirements, subject to geometric and equilibrium constraints. The iterative strategy would require the
information of the required power distribution ratio. The attitude angle of the payload around x-axis, and y-axis, will
incrementally increase. After each increment the force distribution ratio will be calculated, which will be used to calculate
the power distribution ratio. If the resulting power distribution ratio will be similar to the required power distribution ratio,
the iterations will stop. The process is also explained in Algorithm 1.
Let aibe the position vectors of the anchor points of the cable on the payload in the world frame εx0,y0,z0,ribe the center
of mass in the world frame εx0,y0,z0, let ˜
ribe the position vector of center of mass in body frame B˜x,˜y,˜z, and ˜
aibe the anchor
points in the body frame B˜x,˜y,˜zas shown in Figure 1. Let Rbe the rotation matrix determined by the Euler angles of the
κ= [α,β,γ]T. Given the ri,˜
aiand the attitude angles of the payload, it is possible to ﬁnd aiusing the relation
ri). Starting from an arbitrary attitude angle, the world frame location of the anchor points is found. The lift
forces fiat each anchor point in vertical axis are found using the anchor point position ai, equilibrium conditions of moments
around x and y axis, and equilibrium of forces in vertical axis. During hovering condition, the thrust u1ican be written in
the following form fi+wi=u1iwhere wiis the weight of the drone i, and fiis the lift force applied to payload by drone i.
This equation is simply showing that the combined thrust applied by the rotors of each UAV must be enough to support the
UAV‘s weight and the lifting force applied to the payload by the UAV. After calculating the resulting thrust u1iwe can use
Fig. 1. Payload with respect to body and world frame along with anchor points
Eq. 7 for the calculation of the required power pifor each drone. The resulting power ratios, are compared with the required
power ratios. The process is repeated till the payload attitude angles are found for which required power ratio is achieved.
However, when i=2, another method can be used to distribute the load, which is explained in Algorithm 2.
Algorithm 1: Reference load attitude generation for load distribution strategy
Input : r,ai,mp,wi,pi
U pdat e→α,β;
if pi=achieved then
Goto U pd ate
The above strategy is tested for lift load distribution, on a voluminous 3D object of mass 1 Kg carried by 3 UAVs. The
center of mass is assumed to be same as geometric center located at [0,0,0.5] with respect to body frame, while the anchor
points in body frame are deﬁned as [-1,0,1],[-1 0 1],[0 1.7321 1]. Algorithm 1 was tested with several lift load ratios which
it was able to process in less than 6 ms. Lower computational costs mean that there is a possibility to incorporate the strategy
for real-time implementation of the algorithm if the power consumption measurement is available in realtime. The sample
runs of the code are shown in Table 1. Algorithm 1 provided not only the required attitude angles of the payload, but also
the required position of the anchor points in world frame to achieve required load distribution.
Several assumptions are used for the development and testing of the strategy, which are discussed in this section.
1. We assume that the attitude dynamics of the hex-rotors is decoupled from the payload. In experiments, this is valid via
Table 1. Payload attitude angles for lift load distribution
Load ratio computation time (ms) α β
0.25,0.25,0.5 0.03 28.64◦-28.64◦
0.33,0.2,0.46 0.012 -25.78◦-60.16◦
0.3,0.3,0.4 0.005 -85.94◦-11.45◦
using a small cable between the metallic strip and the supporting rod of the payload. The UAV models were simpliﬁed
by assuming that the UAV structure is rigid and both Center of gravity (CoG) and geometric center are at origin U˜a,˜
of body frame of the UAV.
2. All aerodynamic disturbances including the lateral drag on the whole system, the thrust shielding of the payload, are
ignored. A rectangular shaped payload can result in unwanted moments caused by the downwash of the air from the
rotors. This aerodynamic inﬂuence of the payload to hex-rotors are ignored in simulations and was bypassed in hardware
experiments via selecting an equivalent payload. The equivalent payload consists of less surface area but similar relative
location of center of gravity (CoG) from the anchor points.
3. When a point mass or low volume payload is jointly carried by two or more UAVs using cables, the UAVs experience a
pull towards each other . When a voluminous payload is considered with longer lengths, UAVs do not have to apply
lateral forces to stay apart from each other. This also implies that the cable direction are both along the gravity direction
as described in Eq. 3.
4. Propulsion system efﬁciency degradation during ﬂight due to voltage supply decline is ignored. We ignore battery fail-
safe activation due to voltage threshold, to achieve this, experiments are performed within limits of fail-safe activation.
5. We assume equal load sharing for lateral transportation, however the lifting load is distributed amongst UAVs via our
strategy. This is achieved by employing a collaborative transport strategy which works by moving the UAVs in synchro-
2.2 Multi-hex-rotor-payload system model
The system consists of nhex-rotors drone01, drone02... drone-n which will be from now on referred as i=1,2...n,
attached to a payload as shown in Fig. 1. Let pi= [xi,yi,zi]Tbe the position vector of the center of mass of the hex-rotor i
relative to the ﬁxed inertial frame or world frame εx0,y0,z0. The orientation of the hex-rotor iis expressed in Euler angles as
Φi= [θi,φi,ψi]Twhere θiis the roll angle about the y-axis, φiis the pitch angle about the x-axis, and ψiis the yaw angle
about the z-axis of the hex-rotor drone i. Six rotors attached to identical brush-less DC motors are rotating with a speed ωiN .
The following equations best describe the translational and rotational dynamics model used in this paper for the hex-rotor
UAVs which were modiﬁed from . The model from  is modiﬁed to include lift forces fiin vertical axis. Furthermore,
the payload mass was equally shared by nUAVs in lateral transport direction.
φ= (Iiy −Iiz)˙
θi= (Iiz −Iix)˙
ψi= (Iix −Iiy)˙
Where irepresents drone number. miis the mass of the hex-rotor UAV and mpis the mass of the jointly carried payload, and
¨xi, ¨yi, ¨ziare the translational accelerations of the hex-rotor UAV in x0,y0and z0axes. u1iis the sum of thrust TiN produced by
all motors which is calculated as ∑N=6
1TiN,u2iis the moment created by the thrust force around x-axis, u3iis the moment
created by the thrust force around y-axis and u4iis the moment created by thrust force around z-axis, u5iis the signed sum of
speed of rotation of all propellers. The rotational inertia of the drone iis expressed as (Iix ,Iiy,Iiz).Jis the total inertia of the
motor. The payload as shown in Fig. 2(a) is considered to be rigid cylinder of length d, with uniformly distributed mass mp.
Let r= [X,Y,Z]Tbe the position vector of the center of mass of the payload relative to the ﬁxed inertial frame εx0,y0,z0. The
orientation of the payload is expressed in Euler angles as κ
κ= [α,β,γ]Twhere αis the roll angle about the x-axis which is
described as tilt angle and is the main constituent in the load distribution strategy, βis the pitch angle about the y-axis, and γ
is the yaw angle about the z-axis of the payload which are ﬁxed in this case.
(a) Free body diagram of dual-UAV-payload system with payload in tilted
(b) Description of payload geometric parameters
Fig. 2. Payload geometric parameters and freebody diagram
2.3 Power consumption by motors
As in our previous work , an empirical equation between the rotor speed and the power consumption is obtained
using curve ﬁtting as shown in Eq. 7 , for the platform used in this study. The data-set for the empirical equation was obtained
by performing lab tests using thrust stand and was also compared with  for the same propulsion system.
Where PiN is the power consumed by rotor Nof the UAV i, and ωiN is the speed of the rotor in rad/s. The speed of the
rotors is found using the relation TiN =kbω2
iN where kb=9.85 ×10−6, where TiN is the thrust produced by the rotor Nof
multirotor iand kbis the co-efﬁcient of lift of the propellers. The predicted power consumption of two UAVs carrying jointly
equal load while hovering was compared against the experiment results as it will be shown in Section 5.
3 Energy aware collaboration strategy for 2D object
Given the state of charge of two UAVs to be used for joint payload transportation, the mission success and failure is
tested via simulation methodology presented in this paper. Based on the state of charge of the batteries, the load can be
distributed between the UAVs, using the strategy described in Algorithm 2. The steps presented in Algorithm 2 are described
in this section in detail. This section will describe the system model, power distribution, load distribution, the collaborative
transport strategy, and the constraints that should be considered while using this energy distribution strategy.
3.1 Load distribution
Assuming a payload of weight wand length d, and considerable height is carried by two UAVs, via cable as shown
in Fig. 2(a). The mass distribution is assumed to be uniform, hence center of mass of the payload is assumed to be the
geometrical center of the payload. The free-body diagram of the UAV payload system is shown in Fig. 2(a). The lift force
exerted on the payload by each UAV can be found by using Newton‘s 2nd law and summation of moments around the
payload CoG. Let fibe the lift force exerted by the UAVs on the payload, while diis the distance from the center of mass
of the payload to the point of application of force fi. Using moment summations, we know that the ratio f2
f1and ratio d1
equal. Thus the required force distribution can be used to ﬁnd the required moment arm ratios d2
d1. The next step is to ﬁnd
the UAV conﬁguration in air that can provide the desired ratio d2
d1. If the tilt of the payload is α, the sum of d1and d2can be
Fig. 3. Sensitivity of load-distribution potential vs tilt (α), for various aspect ratios (AR)
Where hdis the half of the diagonal length, ϖis the angle of the diagonal with the base of the payload as shown in Figure 2(b).
For a given value of tilt angle αEq. 8 and Eq. 9 can be solved to ﬁnd the ratio, d2
d1, so iteratively, value of αcan be found for
which the d1
d2is equal to the desired ratio. αcan be used to ﬁnd the required elevation of the drones using ∆h=dsinα.
3.2 Load distribution capacity
The load distribution formulation was used to plot the variation of load distribution potential as represented by d2
the tilt angle αfor payloads with different aspect ratio in Fig. 3. The y-axis shows the values between 0 and 1 where 1
represents equal load sharing, 0.5 means that drone02 is carrying half of the load as drone01, while 0 means that drone02 is
not carrying any load. Similarly d1
d2can be plotted to show an increase in the load share of drone02 while decreasing the load
of drone01. Aspect ratio (AR)of the payload, is the ratio of length dof the payload versus the width wpof the payload. The
load distribution sensitivity was found to vary according to the aspect ratio of the payload. As it is shown in Fig. 3, higher
aspect ratios mean that the load distribution is less sensitive to the variation of α. It is also observed that the sensitivity of
load distribution increases signiﬁcantly for higher aspect ratios near 90◦angle. At lower aspect ratios, load distribution is
more sensitive to the variation of the angle α. This implies that the potential of load distribution is higher in lower aspect
ratios. Apart from that, Fig. 3 also presents the limits of load distribution, for example, a payload with aspect ratio of 2,
requires the αto stay below 63◦to have some load distribution, and after 63◦, the load is entirely carried by one UAV. This
is due to the fact that the line of action of lift force of one UAV now passes through the CoG of the payload.
3.3 Power distribution
Assuming that we know the required energy distribution between the two UAVs E2
E1, where E1is the energy level of
drone01 and E2is the energy level of drone02, we can write P2
E1to convert the total energy consumption ratio into
instantaneous power consumption ratio, where P1is the energy level of drone01 and P2is the energy level of drone02. We
can use Eq. 7 for the calculation of the required thrusts u12 and u11. During hovering condition, the thrust u1ican be written
in the following form fi+wi=u1iwhere wiis the weight of the drone i, and fiis the lift force applied to payload by drone i.
This equation is simply showing that the combined thrust applied by the rotors of each UAV must be enough to support the
UAV‘s weight and the lifting force applied to the payload by the UAV.After obtaining the reference thrusts u11 and u12 via
Eq. 7, we can use fi+wi=u1ito ﬁnd the required lift force ratio f2
Fig. 4. Centralized co-ordinated motion collaboration strategy for multi-UAV payload transportation
Algorithm 2: Reference load attitude generation for load distribution strategy
Input : E1,E2,mp,w1,w2,hd,ϖ
Given the available energy values, we ﬁrst determine the required thrust u1i, which should satisfy the constraints,
u1i−max >u1i>wi, because if the required thrust is lower than the weight of the UAV itself, or higher than the maxi-
mum possible thrust, the mission cannot proceed further. Another constraint is the minimum distance allowed lmin between
two UAVs for collision prevention, and also to avoid any aerodynamic inﬂuence of one UAV to the other. lmin can be used
to ﬁnd αmax using dcosαmax >lmin . The minimum distance, can also be inﬂuenced by the possibility of any collision of the
payload with the UAV rotors. The length of the gripper cable can inﬂuence the maximum tilt αmax . If lsbe the safety factor
to deal with position errors and external disturbances, lgis the length of the gripper, lris the distance between the autopilot
(Center of gravity of hex-rotor) and the rotor tip then we can use trigonometric relations to ﬁnd αmax.
3.5 Collaboration strategy
A co-ordinated motion strategy with a centralized trajectory controller is used in this paper for testing the power dis-
tribution mechanism as shown in Fig. 4. The centralized trajectory controller is responsible for synchronized motion of the
UAVs. The centralized trajectory controller, continuously monitors the error between desired pose for both UAVs and the
current pose, when the error is lower than the pre-deﬁned tolerance, the next way-points are sent to both UAVs at the same
time. This collaboration strategy requires fast and reliable communication between UAVs and the central computer. The
control of the multi-UAV system relies on the individual low level position controllers of each multi-rotor. It is therefore
signiﬁcant that these individual controllers are properly tuned. The motion controllers for both drones consist of a position
controller (P), which generates velocity set-points, a velocity controller (PID), which generates the attitude set-points, and
the attitude controller (PID) generates the required motor RPM set-points. This collaboration strategy was tested in software
in the loop simulations (SITL) and laboratory experiments as shown in the video .
4 Simulation methodology and results
The strategy mentioned in this paper although tested on hex-rotor platform, is applicable to multi-rotors and helicopters
in general. Two DJI F-550 models weighing 3.2 Kg each and payloads were simulated using Simulink. Simulink has been
used by [28,29] for modeling of a UAV. The system model as described in Sections 2.2 and 3.1 is implemented using Simulink
for numerical solution of differential equations and modeling of the payload. A complete detail of the model is described in
Fig. 5. The power consumption calculation used in this model is described in Section 2.3. The payload is modeled as a rigid
Fig. 5. A complete description of numerical simulation model components used for the veriﬁcation of load distribution strategy.
body with dimensions as described in Section 3.1, which is subjected to lift force and translational force provided by the
UAVs. The translational force is assumed to be equally distributed between the UAVs, however the lifting force is calculated
using the altitude difference of the two drones using the formulation described in Section 3.1. The collaborative transport
strategy and the motion controllers, used in this Simulink model are described in the Section 3.5. The altitude, attitude and
position controllers were added to the Simulink model and tuned. All controller gains, rotor parameters, UAV inertia, UAV
mass, used in the simulation were taken from . Simulation tests were performed in two stages, ﬁrst stage is the base
case simulations, where the results were compared with the hardware experiments. The second stage is where the simulation
results were extended to test extreme cases of load distribution.
4.1 Base case simulations
In base case simulation, the payload considered was a 586 g rectangular beam with dimensions[1.6×0.5×0.1]m. Two
DJI F-550 models named as drone01 and drone02 were considered with energy levels of 37 kJ and 34.5 kJ respectively. At
ﬁrst the simulation was performed with equal energy distribution. It was found via simulations that in order to transport the
payload, while sharing the load equally, each drone will need 36.9 kJ of energy. The drone02 with 34.5 kJ battery failed
while transporting the payload back and forth from −0.2mto1.4 m in y-axis as shown in Fig. 6(c) in red, and hence the
mission was not successful.
The load distribution strategy as described in Section 3.3 was applied and a 6.2 % power consumption ratio was consid-
ered. Based on this ratio, payload roll angle α=37◦was proposed to achieve a lift force distribution of 62 % and 38 %. A
minimum horizontal distance of 1.32 m, with an elevation difference of 1 m between the drones was required to achieve this
load distribution. All other constraints discussed were satisﬁed. When the lift force distribution was applied, the resulting
power consumption by the two drones was 36.75 kJ and 34.25 kJ respectively, proving the effectiveness of the proposed
strategy. The results obtained via the simulation tests are shown in ﬁrst column in Fig. 6 which can be compared to the
experimental results in the second column as shown in Fig. 6. The details of experimental setup and methodology is ex-
plained in Section 5. The red color represents the transportation with equal load sharing, while black color represents the
collaborative transport with strategic load sharing. The dashed line represents drone02 while solid line represents drone01.
4.2 Extended simulated results
Extended simulation experiments were performed with higher load distributions and energy consumption ratios. Several
collaborative transport experiments were performed with equal load sharing and unequal load sharing as shown in the Ta-
ble. 2. The simulations were performed for payloads of range 0.75−1.5 Kg mass and [1.66 ×(0.75 −1)×0.1]m dimensions.
Two DJI-F550 platforms with energy differences of 19.86,24.84,26,33.12% are considered. Two cases each were simulated
with and without load sharing strategy for each energy state. The Table. 2 shows a maximum of 74−26% lift force sharing
and a maximum of 33.12% of energy sharing. It can be seen in Table. 2 that several conﬁgurations of energy levels result in
mission failure with equal load sharing. Therefore the load sharing is performed based on the proposed strategy which leads
to accomplish the mission successfully.
5 Experimental results and discussions
The system hardware consists of one ground station, and two DJI-F550, with Pixhawk autopilot with PX4 ﬁrmware
activation and deactivation only; therefore it was powered by the same battery that powered the drone. The EPM was
activated and deactivated using a ROS node. A force sensor is placed between the triangular 3D printed frame and magnetic
(a) Drone altitudes in simulation (b) Drone altitudes in experiment
(c) Trajectory of the drones while transporting the payload in
(d) Trajectory of the drones while transporting the payload dur-
(e) Relative positions of the drone during simulations (f) Relative positions of the drone during experiments
(g) Lift force distributions in simulation (h) Lift force distributions in experiment
(i) Power consumptions in simulation (j) Power consumptions in experiment
Fig. 6. First and second column represent similar experimental and simulation scenarios
Table 2. Extended simulations with various payload shapes and energy states
Payload mass shape ∆Eαf1/f2outcome
1Kg [1.66m×1m×0.1m]0% 0◦50% −50% Failure
1Kg [1.66m×1m×0.1m]24.84% 46◦81% −19% Success
1.5Kg [1.66m×1m×0.1m]0% 0◦50% −50% Failure
1.5Kg [1.66m×1m×0.1m]33.12% 46.29◦81% −19% Success
1.5Kg [1.66m×0.75m×0.1m]0% 0◦50% −50% Failure
1.5Kg [1.66m×0.75m×0.1m]26% 46.29◦74% −26% Success
0.75Kg [1.66m×1m×0.1m]0% 0◦50% −50% Failure
0.75Kg [1.66m×1m×0.1m]19.86% 46.29◦81% −19% Success
gripper as shown in Fig. 7(b). The force sensor (FSE1001) is uniaxial force sensor which transmits the force values using
the USB port of the computer mounted on the drone. The data received by the force sensor is converted into force values
using a ROS node and is published on a ROS topic with a frequency of 150 Hz. A ground station with ROS Master works
as centralized station to communicate and command both drones via wireless connection. Three sets of experiments were
conducted to validate the load sharing method for collaborative transport. First, baseline tests were performed to benchmark
the power consumption of both drones as explained in subsection 5.1. After that, the payload was lifted and kept in hover
by the drones while sharing the load in the second set of experiments which are explained in subsection 5.2. In the last
experiment, collaborative transport was performed with equal and uneven load sharing as described in subsection 5.3.
5.1 Baseline test
Firstly a baseline hovering power consumption test was performed. In this test drone01 and drone02 took off together
autonomously and reached the same altitude. The drones were hovering without any external payload while power con-
sumption was recorded which illustrated that the power consumption proﬁle is similar for both drones as shown in Fig. 8. A
similar test was conducted where both drones were jointly carrying the payload while sharing the payload equally. It can be
observed in the Fig. 8 that both drones are now consuming an increased amount of power due to payload addition, and the
amount of power consumed by both drones is similar.
5.2 Load sharing in hover
The aim of load sharing experiment is to observe the effect of spatial conﬁguration of the drones on the power consump-
tion. A payload equivalent of [1.6×0.5]m of mass 580 g is constructed using two lightweight aluminum rods of length
0.25 m rigidly ﬁxed perpendicular to a 1.6 m rod of rectangular cross-section as shown in Fig. 7(d). The aspect ratio of the
equivalent selected payload is 3.32. The mass of the 1.6 m rod being signiﬁcantly greater than the 0.25 m aluminum rods
makes it possible to have the center of mass of the payload at the center of the 1.6m rod, mimicking a beam of [1.6×0.5]m
of mass 580 g. The payload is already attached to the gripper of the drones. The experiment consists of autonomous takeoff
of the drones with payload where both drones are at the same altitude, and then achieving desired spatial conﬁguration for
both drones. The altitude difference of the two drones was selected with an increment of 0.25 m from 0 to 1.25 m. The
experiments were constrained to avoid ground effects while staying within the range of Optitrack system and the payload
weight chosen based on the payload limitations of the drones. Further increase in altitude difference from 1.25 m resulted
in violation of geometric constraint as shown in the experiment video2. The drones start by taking-off together with the
payload, then achieving same altitude, and wait for 5 seconds to stabilize in the same altitude. After that the desired altitude
difference is achieved and maintained for 150 seconds. The instantaneous power consumption for all cases for both drones is
shown in Fig. 9(b), which shows the incremental difference in power consumption by both drones with incremental change
in the altitude. Total energy consumption is then calculated for each drone. A comparison is made between the percentage
difference of total energy consumption between the two drones for each tilt angle αas shown in Fig. 9(a). The total energy
for each case is found by integrating the instantaneous power over the time of ﬂight. After ﬁnding the total energy for each
(a) The drone DJI F550 experimen-
(b) EPM gripper with force sensor
(c) The connections between the computer, autopilot, EPM force sensor
(d) Drone01 and drone02 during experiment
Fig. 7. A description of the hardware used in the experiments. The payload, hex-rotors, gripper with force sensor and EPM is also shown
Fig. 8. Instantaneous power and energy consumption by both drones, with and without payload, where dashed lines represent energy
consumption of drones with payload
drone, the percentage energy consumption difference is calculated and plotted in Fig. 9(a). It can be seen in Fig. 9(a) that
the experimental results are in agreement with the simulated results. These experiments provide the proof of concept of the
load distribution which can be achieved by manipulating the orientation of the payload. It is also observed here that power
consumption varies with time, showing an increase in power consumption of drone carrying heavier load. Future work will
include improvement of the strategy to account for such increase. A video demonstration of the whole set of experiments
can be accessed via link2.
0 10 20 30 40
Energy diff (%)
(a) ∆Evs tilt angle αbetween two drones for simulation and
(b) Power consumption at various tilt angles for corresponding
altitude differences of 0.25-1.25 m, arrows are used to hint the
corresponding pair of drones during one test
Fig. 9. Power consumption during load sharing in hover
5.3 Load sharing in collaborative transport
Two tests were conducted to demonstrate the collaborative transport via load sharing. In these test, drones autonomously
takeoff while jointly carrying the payload to the transportation altitude. In the ﬁrst test, the collaborative transport was
performed via equal load sharing i.e. the transportation altitude set-point was the same for both drones. In the second test,
collaborative transport was performed via uneven load distribution i.e drone01 was given a high altitude set-point compared
to drone02. The second test was aimed to distribute the lifting load 62% and 38% to drone01 and drone02 respectively,
to achieve a 6.2% distribution of energy consumption. This means that if the energy level of drone02 is 6.2% lower than
drone01, the distribution of load will still make the transportation possible. This test is the replication of the simulation
test performed in the previous Section 4.1 with the models using the same hardware. Both tests successfully demonstrated
the to and fro transportation of a 580 g payload in y-axis between −0.2 to 1.2 m in the lab. The test results were obtained
and plotted in Fig. 6. Speciﬁcally, Fig. 6(d) shows the to and fro lateral translational trajectory for the transportation of
the payload. In both cases, the drones are able to transport the payload in a similar manner even when they are unevenly
distributing the lifting force of the payload. The lifting force was distributed via changing the vertical spatial conﬁguration
of the drones relative to each other which is shown in Fig. 6(b). The lift force data obtained by the force sensors is presented
in Fig. 6(h). Fig. 6(f) shows the relative trajectories of the drones in both cases. The instantaneous power consumption for
both drones is shown in Fig. 6(j), which shows similar values as compared to the simulation results shown in Fig. 6(i) and
the resulting energy consumption difference is found to be 6% which is very close to the predicted value of 6.2%. The error
between numerical simulations and experiments, can be attributed to the unaccounted aerodynamic perturbations, caused by
the close proximity of drones while in ﬂight. A detailed video of the collaborative transport with load sharing is available
6 Conclusion and future work
This research study proposed an easy to implement multi-UAV collaborative aerial transport strategy with load distri-
bution capability. A load sharing strategy was proposed to deal with uneven battery levels that can ensure the success of the
mission. The constraints and limitations of the load sharing strategy were also discussed. A simulation was performed using
a hex-rotor-payload model developed using Simulink. The effectiveness of the proposed strategy was demonstrated using
an example case. Several experiments were conducted to demonstrate the effect of spatial conﬁguration of the drones on the
force distribution and the power consumption. Speciﬁcally the experiments demonstrated a 62% −38% force distribution to
achieve approximately 6.2% power consumption distribution using simulations and 6% power distribution in hardware ex-
periments based on two hex-rotors UAVs. Extended simulations were also performed to achieve a maximum of 81% −19%
lift force distribution to distribute the power 33.12% between the two UAVs. Current work could be expanded to load sharing
amongst more than two UAVs. Future work will include the effect of shielding of the thrust airﬂow of rotors by the payload
and how to mitigate such effect.
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List of Figures
1 Payload with respect to body and world frame along with anchor points . . . . . . . . . . . . . . . . . . . 3
2 Payload geometric parameters and freebody diagram . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
3 Sensitivity of load-distribution potential vs tilt (α), for various aspect ratios (AR) . . . . . . . . . . . . . . 6
4 Centralized co-ordinated motion collaboration strategy for multi-UAV payload transportation . . . . . . . . 7
5 A complete description of numerical simulation model components used for the veriﬁcation of load distribu-
6 First and second column represent similar experimental and simulation scenarios . . . . . . . . . . . . . . 9
7 A description of the hardware used in the experiments. The payload, hex-rotors, gripper with force sensor
8 Instantaneous power and energy consumption by both drones, with and without payload, where dashed lines
represent energy consumption of drones with payload . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
9 Power consumption during load sharing in hover . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
List of Tables
1 Payload attitude angles for lift load distribution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
2 Extended simulations with various payload shapes and energy states . . . . . . . . . . . . . . . . . . . . . 10