Cogent Engineering
ISSN: 2331-1916 (Online) Journal homepage: www.tandfonline.com/journals/oaen20
Autonomous recharging of multirotor unmanned
aerial vehicles to extend operational time
Darshan R, Victor George, Dawnee Soman, Prateek N, Sahiti Sree & Ashish H
To cite this article: Darshan R, Victor George, Dawnee Soman, Prateek N, Sahiti Sree & Ashish
H (2025) Autonomous recharging of multirotor unmanned aerial vehicles to extend operational
time, Cogent Engineering, 12:1, 2490529, DOI: 10.1080/23311916.2025.2490529
To link to this article: https://doi.org/10.1080/23311916.2025.2490529
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COGENT ENGINEERING
2025, VOL. 12, NO. 1, 2490529
https://doi.org/10.1080/23311916.2025.2490529
ELECTRICAL & ELECTRONIC ENGINEERING | RESEARCH ARTICLE
Autonomous recharging of multirotor unmanned aerial vehicles to extend
operational time
Darshan R, Victor George
, Dawnee Soman
, Prateek N, Sahiti Sree and Ashish H
Electrical and Electronics Engineering, Ramaiah Institute of Technology, Bangalore, India
ABSTRACT
ARTICLE HISTORY
An automated navigation system is proposed for Unmanned Aerial Vehicles (UAV) to charge
batteries according to their State of Charge (SoC). A compact charging pad suitable for
mounting on street light poles was designed for the autonomous charging of the UAV. The
UAV can land on the charging pad when the State of Charge reaches predefined threshold
values. The landing on the charging pad at predefined GPS coordinate values was found to
be satisfactory during actual testing.
Received 30 September 2024
Revised 2 December 2024
Accepted 23 December 2024
KEYWORDS
Charging pad; flight mission
planning; SoC; re- charging;
street light poles; UAV
SUBJECTS
Design; Testing; Transport &
Vehicle Engineering;
Systems & Control
Engineering; Power &
Energy
1. Introduction
Unmanned Aerial Vehicles (UAVs) have emerged as
essential tools in various applications, including surveillance, delivery, aerial photography, sample collection, agriculture, and infrastructure inspection. The
operation time for these applications requires a longer battery life for UAVs. However, the limited flight
longevity of UAVs necessitates the design of alternate charging mechanisms. Charging can be wired
or wireless. It is crucial to maintain the battery
charge of the UAV to the required level without
interrupting the mission. The charging stations for
the UAVs can be distributed at different locations.
The task of identifying the charging requirement
and allowing the making the UAV to reach the
charging station on time is crucial. Establishing
proper contact between the UAV battery and the
charger is an important task. Precision landing techniques and charging mechanisms are the major concerns in this regard. An automated camera-based
drone landing system utilizing computer vision algorithms for precision landing is reported in (Demirhan
CONTACT Victor George
victorgeorge@msrit.edu
& Premachandra, 2020). The system employs visual
markers or fiducial tags on the landing platform,
enabling the UAV to navigate and land accurately.
The system architecture, algorithms, and experimental results are discussed, highlighting its potential for
extending UAV operations.
The automated battery charging system discussed
in (Voznesenskii, 2021) is specifically designed for
multi-rotor aerial vehicles. The system incorporates
intelligent charging algorithms and a charging pad
equipped with wireless power transfer technology.
The authors discussed the design considerations,
charging process, and efficiency analysis, demonstrating the feasibility of autonomous battery charging for UAVs. An efficient charging pad for
unmanned aerial vehicles based on direct contact
charging is proposed (Al-Obaidi Eta, 2018). The
charging pad uses conductive plates to establish a
direct electrical connection with the charging contacts of the UAV. The precision landing option for
unmanned aerial vehicles discussed in (Janousek &
Marcon, 2018) proposes a combination of visual and
altimeter-based landing approaches to achieve
Electrical and Electronics Engineering, Ramaiah Institute of Technology, Bangalore, India
ß 2025 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits
unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The terms on which this article has been published allow
the posting of the Accepted Manuscript in a repository by the author(s) or with their consent.
2
D. R ET AL.
accurate and safe landings. The system architecture,
landing algorithms, and experimental validation are
discussed, emphasizing the importance of precise
landing capabilities for UAV operations. The study in
(Priandana et al., 2020) focuses on the development
of an autonomous UAV quadcopter using the
Pixhawk flight controller and the acquisition of flight
data. This study demonstrates the feasibility of
autonomous flight and data acquisition for UAVs,
laying the foundation for extended operational capabilities. A flight mission planning for battery-operated autonomous drones is discussed in (Alyassi
et al., 2023). They proposed an integrated system
that incorporated intelligent algorithms for both
recharging and mission planning. The system optimizes the flight paths of the drones, considering factors such as battery levels, mission objectives, and
environmental conditions. Wireless charging technologies are getting more attention in recent days
(Chittoor et al., 2021). A building integrated photo
voltaic-based wireless charger proposed in (Chittoor
& Chokkalingam, 2024) utilizes solar power to charge
the UAV through a wireless charging mechanism
without addressing the precision landing.
Tracking of UAV can be utilized in many applications. Configuration of feasible paths from a given
start location to a goal for multiple unmanned aerial
vehicles operating in an obstacle-rich environment is
reported in (Sujit & Beard, 2009). Since the UAVs
have limited sensor and communication ranges,
when they detect a pop-up or a moving obstacle
that is in the collision course with the UAV flight
path, it is suggested to re-plan a new optimal path
from its current location to the goal. Placement of
charging stations using an algorithm is reported in
(Ahmed et al., 2016).
Various studies collectively provide insights into
the development of automatic platforms for landing
and charging of UAVs, showcasing various
approaches, technologies, and algorithms used to
extend UAV operations. A ‘multi-criteria objective
function’ has been defined in (Alyassi et al., 2023) in
completing a flight tour mission with pre-defined
target sites ensuring minimum time duration
between intermediate recharges. A rectangular wireless inductive charging pad was proposed in (Khonji
et al., 2017) for the autonomous charging of the
drone. A battery replacing solution for a UAV after
its automatic landing is proposed in (Chen et al.,
2023). The initiation of the charging requires an
external signal from the user. Charging can be either
wired or wireless. A wireless power transfer for
autonomously charging a drone is reported in
(Raveendhra et al., 2020). Network of wired charging
stations is proposed in (Kokkinos et al., 2023) using
the LoRa communication system and a pressure sensor system to identify the drone entry to the station.
A mission planner was simulated in (Boucek & Flıdr,
2024) to obtain the optimal battery replacement
schedule using A algorithm. Optimal control and
guidance of unmanned aerial vehicles can be executed through the Ardupilot, ArduRover and Mission
Planner platforms in which simulated gyroscope and
accelerometer data, are utilized (Kawamura &
Azimov, 2019; Prasetia et al., 2019; Romanov et al.,
2023). The location of the battery charging stations
is crucial in completing the mission of UAVs without
any interruption (Nieuwoudt et al., 2023). A power
line charging drone is proposed in (Ben-Moshe,
2021) and a network of charging stations on the
roof of the buildings are suggested in (Raciti et al.,
2018). Recent reports show the probabilities of
drone to drone energy transfer in order to reduce
the travel time towards the charging stations
(Jaiswal and Bhunia, 2024) and an ‘In Flight Wireless
charging’ is introduced in (Chen & Zhang, 2024).
The prior studies have explored different aspects
of the field, including camera-based landing systems,
battery charging infrastructure, charging pad design,
precision landing options, autonomous flight control
using Pixhawk controllers, and mission planning for
battery-operated drones. It has been found out that
many of the UAV start-ups have proposed this idea
of charging a drone using a docking station or a
charging pad. Apparently, these firms have their
own flight computer designed which does not make
the charging pad they have designed universal for
other drones.
Wired charging can ensure minimal energy loss
and faster charging which are very critical in managing the successful completion of the mission. But
aligning the UAV charging terminals to that of the
pads is the challenging task. However wireless charging on other hand will not have issues of aligning
the UAV to the exact terminals, but the energy loss
is usually 40% higher relative to wired charging.
The guidance algorithms reported in the literature
ensure that the UAV always maintains a fixed distance from the street mounted charging pads. No
major literature considered the situation where in
the UAV has to go far away from the pads, and the
algorithm ensuring the flight the required amount of
battery charge before the mission starts.
Additionally, the on board flight controller of the
UAV should ensure distance remaining for flight
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based on the current State of charge of the battery
and aborts the mission if the destination is too far.
Camera based precision landing system can be
used when GNSS based landing system fails. A special markers called the ArUco, which is part of a
Fiducial tag are quite frequently used for the purpose where a camera fit on to the drone can decode
the location of the markers that are on the pad and
upon detecting the marker the drone can orient and
lock onto its position and perform a precise landing.
Reliability and readily available OpenCV implementation make ArUco markers a favourite choice among
the robotics community. But the limitations of camera based landing under challenging weather conditions can be addressed to an extent by using GPS
based precision landing. Hence a GPS based precision landing algorithm was tested in the proposed
work in order to analyze the performance.
It is proposed to use a universal charging pad
capable of fixing on the street light poles for the
autonomous landing of the city surveillance drones
as shown in Figure 1, when reaching a preset value
of the battery SoC. The benefits of making a universal charger include uniformity in the mechanical and
electrical design of the UAV, easy communication
with any UAV in the air, and seamless possibility of
UAV to UAV communication. Conditions such as the
charging pads are preoccupied while another vehicle
wants to charge, can be handled by the communication between the charging station and the UAV for
which the UAV can decide in advance about the
ideal path to be chosen to find an empty charging
pad. The challenges however are to implement a
common standard with all the UAV manufacturers in
order for it to be universal. The standards for flight
code incorporating common path planning algorithms, usage of batteries, mechanical design of the
UAV frame and common communication protocol
and telemetry frequencies.
An algorithm that incorporates locations and distances of the charging pad to its path planning algorithm will greatly optimize the UAV operation as the
3
UAV is made to track its path by always maintaining
a fixed threshold of distance from the charging pads
and additionally calculating the distance that can be
travelled using the live state of charge of the battery. The proposed universal charging pad is capable
of charging the drone irrespective of its make, provided some basic information are installed prior to
the mission.
2. Prototyping of multirotor UAV
The hardware part of the multirotor involves a
Quadcopter with X frame with a radius of 250 mm.
The avionics includes four 920RPM/V Brushless DC
motors for thrust and four 30 A sensor less electronic
speed controller. All four motors are powered by a
single 3 cell 2200mAh Lithium polymer battery. Each
motor was fit with a 10 x 4.5" propeller to provide a
total thrust of approximately 2400 g.
Pixhawk 2.4.6 is used as the flight controller, running Arducopter as its firmware. UBLOX M8N is used
as GPS receiver and gives horizontal dilution of precision of 0.7 with 17 satellites locked in. The flight
controller is communicating with the ground control
station via a 915 MHz telemetry, and for manual controlling the drone uses a 2.4 GHz receiver which is
controlled by a 2.4 GHz, 16 channel transmitter.
Major components used to build the prototype system is shown in Figure 2.
The data logging, path and mission planning is
done using Mission planner ground control station.
Open source Ardupilot and Lua script are used to
develop the autonomous navigation system.
The multirotor is simulated using the software in
the loop simulation (SITL) in order to verify the program and the mission plan that is uploaded on to
the flight controller so as to prevent any unknown
crash during the outdoor test. The charging platform
on the other hand is a cluster of all the application
circuits available in the datasheet of the individual
chosen IC’s. The landing on the coordinates of the
charging pad is approximately accurate to 0.1 m in
Figure 1. Visualization of the proposed charging mechanism (Not to scale).
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D. R ET AL.
Figure 2. Hardware components of the UAV. (a) QUAD X Frame. (b) 920kv BLDC motors. (c) Pixhawk with GPD module. (d)
915MHz Telemetry.
Table 1. Description of the parameters used for the drone via mission planner.
Options
Auto
MIS
SIM speed up
Disarm delay
SIM Battery
Parameter description
Allows for arming in auto mode
Allows for continuing after land. This is important to get the accurate location of the charging pad
Increasing the simulation time of the mission
Time it takes for the drone to disarm after landing
Simulation battery voltage to be given
the SITL. The distance error is calculated using Lua
scripts by distance monitoring of drone to the charging pad.
Table 1 shows the description of the various
parameters used for the drone via mission planner
to execute the mission. Mission Planner is a
Windows compatible, comprehensive ground station
application for the ‘ArduPilot’ open source autopilot
project. The mission planner user interface is shown
in Figure 3.
The mission plan to execute the proposed project
is as follows.
Configure an autonomous way point mission: A
simple mission is configured where the drone
takes off to 10m navigates to few waypoints loiters around a waypoint for around 20turns and
then autonomously lands at a different waypoint.
Continuously monitor the state of charge of the
battery: The state of charge is continually
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Figure 3. Mission Planner user interface.
monitored using coulomb counting method and
the initial SoC when powered up is taken from
the OCV-SoC curve of the LIPO battery.
Land on the charging pad when the SoC reaches
below threshold: Whenever the SoC reaches
below a threshold that varies for different batteries the drone navigates itself to a pre-programmed coordinate where the landing pad is
located and lands.
Charging pad configuration: The charging pad
sets up the power that has to be output from it
to charge a particular battery on the drone, the
information about the battery (mAh, rated voltage, and number of cells) that has been used on
the drone is shared through NFC tags as soon as
the drone lands on the charging pad.
‘Charge and continue’ mission: The SOC is again
monitored continuously as the drone charges and
at the instant the SOC reaches rated voltage, the
drone takes off and continues its mission.
Block diagram of the flight controller to accomplish the planned mission is shown in Figure 4 and
the flow chart for the mission plan is given in
Figure 5.
3. Autonomous recharging
The battery charger design is consists of a Power
Factor Correction (PFC) controller, Flyback converter,
and CC/CV control to follow the industry standards
and requirements (IEEE Standard for the Design of
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D. R ET AL.
Figure 4. Block diagram of the Flight controller.
Figure 5. Flow chart of the Mission Planner.
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Chargers Used in Stationary Battery Applications,
2022). The use of a PFC controller in an AC to DC
battery charger has several advantages. Firstly, it
improves the efficiency of the charging circuit, which
means that less energy is wasted during the charging process. Secondly, it reduces the amount of harmonic distortion generated by the charger, which
can cause interference with other electrical equipment. The charging platform incorporates a PFC controller for maintaining the power factor close to
unity and the cost/kwh will reduce and the efficiency
of the charger will greatly increase. This design takes
AC input of 220–240 V and gives output DC power
up to 150 W with a nominal DC output voltage 24 V
and 6 A output current. An AC to DC battery charger
equipped with a PFC controller has the potential to
boost the power factor and efficiency of the charging circuit, thereby reducing harmonic distortion and
energy consumption. Here, an active PFC in the
Critical conduction mode (CRM) was used. The
FAN7930B is an active power factor correction (PFC)
controller for boosting PFC applications that operate
in critical conduction mode.
The UCC2871x family is a flyback power supply
controller that provides accurate voltage and constant current regulation with primary-side feedback,
eliminating the need for opto-coupler feedback circuits. The controller operates in discontinuous conduction mode with valley-switching to minimize
switching losses. These devices use the information
obtained from auxiliary winding sensing (VS) to control the output voltage and do not require optocoupler/TL431 feedback circuitry. Eliminating the
opto-coupler feedback reduces component count
and makes the design more cost-effective. LD1084 is
a low-drop voltage regulator that provides a current
of 5 A of output current. Dropout is guaranteed at a
maximum of 1.5 V at the maximum output current,
decreasing at lower loads.
AD8001 is a low power, high-speed amplifier
designed to operate on ±5 V supplies. AD8001 features a unique trans- impedance linearization circuitry. This allows it to drive video loads with
excellent differential gain and phase performance on
only 50 mW of power. The AD8001 is a current feedback amplifier and features gain flatness of 0.1 dB to
100 MHz while offering differential gain and phase
error of 0.01% and 0.025 . The application circuit
from the each of the main IC’s is modified so that
the output voltage equals 26 V with 16 A.
The PFC circuit corrects the input AC voltage
before it is converted to DC, and the output voltage
of the flyback converter is then filtered to remove
7
any ripple in the signal. The resulting DC voltage is
used to charge the battery.
3.1. Charging pad prototype
The proposed charging pads are suitable to mount
on highway street light poles as the power can be
taken from the same source used to power the
street lights. The challenges posed while mounting
these poles are nearby buildings and trees. Hence
sufficient clearance is needed during landing. To
counter this problem, it is proposed that mounting
the charging pad on only highways as most of the
street light poles on them are cleared of any of the
prior mentioned obstruction. Additionally, the UAV
would recharge fully from a nearest charging pad
before entering a residential area. The distribution
strategy of the charging pads will be dependent on
the specific application. The hardware design of the
charger is made such a way that it can be easily
fixed on to a street light as shown in the CAD model
in Figure 6. The diameter of the charging pad was
fixed as 2 m so that it can accommodate majority of
the drone sizes.
There are groves in between the contacts of the
charging pad so that the contacts should not be
short circuited during rain. In case when the drone
lands in an opposite polarity there is a servo mechanism that reverse the polarity. The prototype charging pad developed is shown in Figure 7. Aluminium
contacts are used for charging the battery through
the pad. The quad X frame is chosen as it is widely
available and low cost, trusses are provided to its
arms to reduce weight and provide strength at the
same time.
3.2. Entire system description
The proposed autonomous recharging system
focuses on landing the drone on the charging pad
solely through the GPS receiver that has approximately 0.7 hdop. The drone lands on the charging
pad to get the accurate coordinates before the mission starts, and the coordinates of the HOME location gets updated in the Arducopter. HOME is the
location where the drone gets ARMED. Charging of
the drone after landed on the prototype charging
pad is shown in Figure 8.
The 920RPM/V BLDC motors along with a 10 inch
propeller provide 900 g of thrust each, thereby making the total thrust from all the four motors as
3600 g. The communication from the drone to the
ground control station is done via micro air vehicle
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D. R ET AL.
Figure 6. CAD model of the charging pad. (a) Groves in pad. (b) Servo mechanism. (c) Charging pad mounted on street light.
Figure 7. Prototype models of (a) charging station. (b) charging pad contacts.
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Figure 8. Charging of the drone after landing.
protocol (MAV link). 915 MHz telemetry is used for
communication with the baud rate of 576,00b/s.
Pixhawk is used as the main flight controller. ublox
M8N module is used as the GPS receiver. 3300mAh
lithium polymer battery is used as the main power
source. The drone also has to calculate whether the
SOC remaining is enough for it to travel to the nearest charging pad. The following algorithm was used
to ensure the optimal operation flight mission considering available SOC of the battery.
3.3. Algorithm for optimized operation
Contact based charging methods have high charging
rates and the energy transfer can also be done with
minimal loss, as opposed to wireless charging.
Techniques like battery swapping, and charging
from the power line involve landing issues, compatibility issues with ground control stations, high computational power and sophisticated signal processing
(Mohsan et al., 2022). Majority of the recent publications have focused on wireless charging but for a
growing and safety centred environment like the
UAV, faster and energy efficient charging is desirable
which can be brought through wired charging techniques. Wireless charging draws attention in recent
years with the emergence of new technologies
(Chittoor et al., 2021). But lack of standardization
and issues in the sustainable power sources for the
wireless technology kept the application at the
infant state.
The primitive method of landing using Global
Navigation Satellite System (GNSS) requires
extremely precise GNSS receivers with very low dilution of precision as the charging pads are quite
small only to accommodate the UAV frame. Precision
landing using computer vision suffers from environmental conditions such as fog and darkness.
In order to enhance these landings advancements
in real-time kinematics have been made that give
accuracy up to centimetres. By considering data
from a reference GNSS of a fixed base station the
error of the moving UAV can be reduced. In cases of
GNSS inaccuracy or conditions where GNSS receivers
or not able to receive in bad weather, IR beacon
techniques can be used to lock on to the charging
pad fit with a transmitting beacon and execute a
precise landing.
Considering six series connected, 10,000mAh battery for a multirotor UAV, at a required charging rate
of 0.5C, a charging pad mounted on the street light
will have a 150W DC charger unit, taking power directly from the 220V mains supplied to the street
lights. The charging pads are equipped with a GSM
module and GPS module through which the drones
can contact the individual pads and receive their
Latitude and Longitude coordinates along with their
STATUS whether the pad is occupied by some other
vehicle.
The algorithm presented is solely for a prototype
test case where in we had placed a charging pad in
an unknown location (unknown latitude and longitude) and we had to feed the location of that pad
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D. R ET AL.
to the drone. Since a communication setup between
the pad and drone was not set up during the experiment, initially manual landing was performed on the
pad and allowed the drone to take its location. After
initialized the location, the entire mission was kept
in the AUTO mode.
The following is the step-wise algorithm used for
the operation of the entire system:
Stage 1: Getting the initial voltage of the drone
and resetting the drone SOC count to the SOC
given from the voltage-SOC function. Then set
the drone to auto mode and the drone starts the
mission.
Stage 2: Obtain the location of the charging pad
after drone lands on the charging pad.
Stage 3: Monitor the remaining SOC of the battery, and the average velocity of the drone with
time of flight to get the remaining distance that
the drone can travel from the nearest charging
pad. The remaining battery capacity is calculated
as follows:
SoC
ðBattery CapacityÞremaining ¼ Battery Capacity
100
(1)
where, Battery Capacity is the capacity of the drone
battery, measured in Watt- hours. The SoC is the current state of charge of the battery, measured as a
percentage. The energy consumption rate of a drone
can be estimated based on various factors such as
the drone weight, the aerodynamics, flight speed,
and propulsion system efficiency. A commonly used
method for estimating the energy consumption rate
is to measure the current and voltage of the battery
during flight and then calculate the power consumed by the drone. To calculate the energy consumption rate, we need to divide the power
consumption by the distance travelled by the drone
during a certain time interval. The energy consumption rate in usually expressed in units of energy per
unit distance, such as Wh/km or Wh/mi. The equation for energy consumption rate can be expressed
as
Power Consumed
time
Energyconsumption rate ¼
distance
3800
(2)
where distance is the distance travelled by the drone
during the time interval, and time is the duration of
the time interval in seconds.
Note that the energy consumption rate may vary
depending on various factors such as the flight
conditions, the battery condition, and the efficiency
of the propulsion system. It is essential to monitor
the energy consumption rate regularly and adjust
the flight parameters. The distance that the drone
can travel based on the remaining battery capacity
and the energy consumption rate of the drone can
be calculated as follows.
Battery Capacityremaining
(3)
Distanceremaining ¼
Energyconsumption rate
The actual distance remaining for the mission
with the available SOC may vary depending on the
specific characteristics of the drone and the flight
conditions.
Now if the remaining distance that the drone can
travel is less than the distance of the drone from the
nearest charging pad then the drone goes to
the nearest charging pad to charge the battery, i.e.
the next stage.
Stage 4: Obtain the destination to the nearest
charging pad location, if the drone lands on the
charging pad, then move to the next stage
Stage 5: Monitor the SoC while the drone is
charging and if the drone is charged then the
mission is resumed and the drone continues on
its mission path again, then revert back to
stage 3.
Monitoring the state of charge of the battery real
time, OCV-SOC curves can be used for smaller
drones, Coulomb counting, Kalman filter and neural
network techniques can be used for larger drones.
Using the remaining SOC, the distance that can be
travelled is calculated using (3), where the energy
consumption rate is given as given in (2). The event
for landing on the pad is triggered when the distance to the next landing pad or the next waypoint
is greater than Distanceremaining. The LLA (Latitude,
longitude and Altitude) of all the landing pad enroute is pre-fed to the flight controller. After landing
on the pad, the information of the UAV’s battery is
fed to the pad and the charging begins, the SOC
monitoring will continue and when fully charged,
the mission is resumed.
The software in the loop simulator of the mission
planner ground control station is used for simulating
the flight mission. The necessary flight parameter
that will govern the in-flight characteristics and initial boundary conditions including some predefined
commands were changed as shown in Figure 9.
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between the location where actual pad was
located and the place where the actual landing
took place. This is due to completely relying on
the GPS co-ordinates for landing.
The SoC estimation by Coulomb counting when
compared to OCV -SoC curve had an approximate
error of 5%. As in the OCV- SoC table the SoC
values are discrete, hence we do not obtain the
continuous SoC values from the table and hence
the error.
The charging time when charged though the
brush contacts increased by 10min when compared to direct wire to wire connection from the
charger to the battery. The possible reason for
this includes poor contact, and impurities on the
metal pads.
The SoC is monitored every 500ms and hence
the triggering of the RETURN TO HOME or RTL
command occurs almost instantly when the SOC
drops below the threshold value.
Figure 9. Parameters set for the drone via mission planner
to execute the Mission.
4. Performance analysis
The prototype is tested initially with wired charging
method. The simulation done in the software in the
loop of the mission planner is compared with the
actual flight test and the following results are seen:
The landing on the coordinates of the charging
pad is approximately close to 0.1m in the SITL.
The distance error is calculated using Lua scripts
by distance monitoring of drone to the charging pad.
The landing on the coordinate of the charging
pad is approximately accurate to 0.8m in the
actual flight test when measured physically
The left side of Figure 10 shows the battery SOC
on the Y-axis and time and flight modes on the Xaxis and the right side of the figure shows the satellite view of the mission undertaken.
Figure 11 shows a graph of the altitude from the
barometer sensor on the Y-axis and time on the Xaxis on the left side. Below the threshold, the mode
should be switched to RTL and land at waypoint
two. As can be seen from the graph the barometric
height exhibits many oscillations owing to the
changing wind conditions and as the RTL triggers
the drone approaches waypoint two and starts
descending at the rate of 15 cm/sec. Figures 12 and
13 show the longitude and latitude coordinates on
the Y-axis and time on the X -axis including the
flight modes.
The oscillations in the middle are the coordinates
of the drone while it is autonomously loitering
around waypoint seven. The immediate end points
of the oscillations are the coordinates of the charging pad. The left end of the oscillations is the location of the charging pad acquired by the drone
before starting its autonomous mission, and the
right end of the oscillation is the location on which
it is actually landed. As can been seen there is an
error in the landing position as it is relying only on
the GPS coordinates to land. The drone is set to
autonomously loitering around the waypoint seven
at 10 m altitude. After the landing the information of
the battery available on the drone will be exchanged
to the charger through NFC tags and the charger
sets the appropriate setting and the charging
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D. R ET AL.
Figure 10. Left side graph shows battery SOC on Y-axis and time and flight modes on the x-axis and Right side plot shows
the satellite view of the mission undertaken.
Figure 11. The graph of altitude from the barometer sensor on the Y axis and time on the X-axis on the left and right sides
shows the satellite view of the mission.
Figure 12. The latitude coordinates on the Y-axis and time on the X -axis including the flight modes.
Figure 13. The longitude coordinates on the Y-axis and time on the X -axis including the flight modes.
COGENT ENGINEERING
process begins. After the SOC is reached to about
95% the charging is cut off and the drone continues
with its mission.
In the proposed UAV charging mechanism, the
navigation and guidance selected, can ensure that
the UAV remains in the safe perimeter of the nearest
pad. Whenever the SOC reduces below a critical
level just enough to reach a nearby charging pad, a
landing sequence is initiated to that charging pad.
In case the SOC remaining is not sufficient to reach
the nearest charging pad, a controlled landing
sequence is automatically executed at the current
position. In case of an uncontrolled situation
detected by the on-board IMU, the power to the
motors will be automatically cuts off thereby preventing the motors from spinning and prevents further collateral damage.
5. Conclusion
Development of battery recharging infrastructure
should align with the growing demands of the
drone industry. Adequate charging stations, strategically positioned in urban areas, remote locations,
and along flight routes, can facilitate seamless operations, quick turnaround times, and enhance the scalability of UAVs. Even though the landing on the
coordinate of the charging pad was found approximately accurate to 0.8 m in the actual flight test,
when measured physically between the location
where actual pad was located and the place where
the actual landing took place, there is a scope for
improving the accuracy using better receivers. The
error is mainly due to the low-end GPS receivers
used for the demonstration. However, use of better
receivers with better antenna along with real time
kinematics (RTK) can significantly reduce the error to
centimeters range and additionally with the help of
camera-based landings the error can be bought
down to absolute zero. The various safety precautions adopted in the proposed GPS based design
make it more adaptable to city surveillance systems.
Collaborations between drone manufacturers, energy
providers, and regulatory bodies is necessary to
establish standards, guidelines, and regulations for
safe and efficient recharging practices.
Even though reliability and readily available
OpenCV implementation make ArUco markers a
favourite choice among the robotics community in
recent years, the proposed GPS based precision landing can ensure better safety in bad weather
conditions.
13
Ethical approval
This Research involving no animals, their data or biological
material that no ethical approval is required.
Consent to participate
This research involves no human subjects directly participated. No consent was required.
Consent to publish
The authors affirm that this work does not contain any
individual person’s data in any form.
Authors’ contributions
All authors contributed to the study conception and
design. Circuit Design and analysis were performed by
Darshan R and Prateek N. Prior art and ideation was done
by Dawnee Soman and Victor George. Darshan R, Prateek
N, Sree Sahiti and Ashish H were involved in the conduction of experiments. The first draft of the manuscript was
written by Victor George and all authors commented on
previous versions of the manuscript. All authors read and
approved the final manuscript.”
Disclosure statement
No potential conflict of interest was reported by the
author(s).
Funding
No sponsorship or funding received for this work.
About the authors
Darshan R, completed his under graduate degree in
Electrical and Electronics Engineering from Ramaiah
Institute of Technology Bangalore, India. His research interests include Unmanned Aerial Vehicle, Battery
Management and Control System.
Dr. Victor George is working in the Electrical & Electronics
Engineering department of RIT, Bangalore.He completed
his PhD. from Visveswaraya Technological University
Belgaum. His areas of interest are Energy Management, AI
applications, DC bus, micro grids and smart grids.
Dr. Dawnee Soman is working in the Electrical &
Electronics Engineering department of RIT, Bangalore. She
obtained her PhD from IISc Bangalore. Her areas of interest
are power and control, nanotechnology, MEMS, and
embedded system.
Prateek N, Completed his under graduate degree in
Electrical and Electronics Engineering from Ramaiah
Institute of Technology Bangalore, India. His research
14
D. R ET AL.
interests include Power Electronics, Battery Management
and Embedded Systems.
Sree Sahiti, completed her under graduate degree in
Electrical and Electronics Engineering from Ramaiah
Institute of Technology Bangalore, India. Her research
interests include Power Electronics and Embedded System.
Ashish H, Completed his under graduate degree in
Electrical and Electronics Engineering from Ramaiah
Institute of Technology Bangalore, India. His research interests include Electronic Circuits and Battery Management.
ORCID
Victor George
http://orcid.org/0000-0002-3401-9792
Dawnee Soman
http://orcid.org/0000-0002-8690-8575
Data availability statement
The data that support the findings of this study are available from the corresponding author, [Victor George] upon
reasonable request.
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