Surface-Condition Detection System of Drone-Landing Space using
Ultrasonic Waves and Deep Learning
Masatoshi Hamanaka, and Fujio Nakano

Abstract— We propose a system for detecting the surface
conditions of a landing space using ultrasonic sensors mounted
on a drone. The advantage of ultrasonic sensors is that they are
extremely low cost, are much lighter and smaller than cameras,
have millimeter-wave lasers, and use LiDAR. However, normal
ultrasonic sensors can only measure the distance from thenearest object, so the amount of information is insufficient toestimate the conditions of a landing space. Therefore, we
propose installing an ultrasonic sensor on each arm of the drone
and estimating the condition of the landing space from the timeseries of reflected waves for very short ultrasonic waves. In themeasurement results, reflected waves were small and changed
irregularly for each sensor where a space was not suitable forlanding. In a simulation experiment using deep learning, oursystem was able to determine whether a condition was suitable
for landing with an accuracy of 98%.

I. INTRODUCTION
We propose a system for small lightweight drones to landsafely at unknown locations. Some drones have a ‘go home’feature that automatically returns them to their take-off
positions if they lose communication with the base station orreceive a return signal. However, drones often crash due torunning out of battery while returning. Additionally, whenreturning, they return in a straight line instead of the route theyflew, so if there is an obstacle along the return flight path, they
will crash into it (Fig. 1). In some cases, the risk of a crash canbe reduced by having the drone land as soon as possible rather
than return to the take-off position.
Small lightweight drones are easily unbalanced by thewind and can crash when landing. For example, in an area ofthick vegetation, leaves can easily wind around a propeller justbefore the drone lands. In addition, if the landing space isrocky, the drone may come into contact with the rocks during
landing and lose balance.
Systems using a QR code or marker have been proposed
for landing drones safely [1–3]. However, such systems
assume a flat landing space. Therefore, if the drone is swept
away by the wind or an abnormal event occurs thatnecessitates an emergency landing, the drone needs to evaluate
the surface conditions of the landing space.
Systems for preventing collisions have been proposed,
including those using Visual Simultaneous Localization and
Mapping (SLAM) [4, 5], range-finding sensors [6], lightdetection and ranging (LiDAR) [7, 8], and stereo cameras [9,10]. Although these systems are mainly used for detecting
obstacles in the direction of drone movement, they may beable to evaluate the surface conditions of the landing space by
Masatoshi Hamanaka is with RIKEN, Center for AdvancedIntelligence Project, Japan (e-mail: masatoshi.hamanaka@ riken.jp).
Fujio Nakano is with Kyoto University Unit of Synergetic Studies
for Space, Japan (e-mail: naknet1@me.com).

Figure 1. Example of drone crashing while returning home

determining the sensor-installation direction. In a system that
estimates the position from the ground surface shape, however,the LiDAR sensor is mounted pointing downward, enabling it
to sense the landing space [11]. However, LiDAR adds a large
payload to these systems, so they are difficult to apply to small
lightweight drones.
A lightweight automatic landing system has been achievedby image processing using four laser pointers and a camera[12]. However, it estimates aircraft altitude under theassumption that the landing surface is flat. Additionally, thislanding system has difficulty finding obstacles with laser
pointers because the laser pointers are pointed straightly andso cannot find obstacles that are not directly in front. In
contrast, ultrasonic sensors cover a wider area than laser
pointers and can receive the reflected waves, so they aresuitable for examining the safety of the landing area.
Therefore, we use ultrasonic sensors to determine whether
the intended landing space is suitable. Their main advantages
are that they are small, lightweight, and low cost. However, aconventional ultrasonic sensor can measure only the distanceto the nearest object, and this information is insufficient toestimate the surface conditions of a landing space [13, 14].
Therefore, we propose a system that has the following fourfeatures.
• Short ultrasonic pulses
We have made it possible to detect both obstacles andthe ground by making the pulse emitted from anultrasound sensor extremely short. There is a method
for recognizing multiple objects with one ultrasonicpulse, but a high-voltage pulse of 720 V must be
generated so that the reflected wave increases quickly[15]. Such high voltages are difficult for drones togenerate. Although there is a method that enables
multiple objects to be recognized by using a single
sine wave of 5 V, fast Fourier transform needs to be
carried out on the host computer, and processing on asmall drone is difficult [16].

••))))))))))))))))))))))))•

Improvement in voltage increase through reflectedwave integration
In our system, ultrasound transmission units emit two
ultrasonic pulses of 5 V. Since the received reflected
waves are very small, they are amplified andintegrated by using an operational amplifier so that apeak appears. Thus, the object can be visually
recognized from this peak.

Ultrasonicwaves

Point cloudY

Z

Ultrasonic receiving unit for each arm
By installing an ultrasonic receiving unit on each armof the drone and observing differences in the peaks of
the acquired reflected waves, the flatness of thelanding point can be determined.

Figure 2. Point cloud from ultrasonic sensors on drone

Suitability identification by deep learning
By using deep learning, it is possible to automatically
identify whether a space is suitable for drone landing[17, 18]. The significance of using deep learning for
determining suitability for landing is twofold: robust
identification and reduced size and weight. For theformer, suitable landing spaces can be robustly
identified by accumulating data on successful andunsuccessful landings. For the latter, by carrying out
deep learning on a single-chip field-programmablegate array, even a small lightweight drone can evaluate
the landing space through autonomous flight [19].

A. Lack of GPS Accuracy
Since global positioning system (GPS) has an error of
about 2 m, 3D shapes cannot be superimposed when creatinga point cloud. By using real-time kinematic (RTK)-GPS [20],the accuracy will be within several centimeters and a point
cloud can be created, but RTK-GPS is difficult to mount on asmall lightweight drone.
Therefore, we do not create a point cloud but determine
whether the surface conditions are suitable for landing. For
conditions in which landing is not possible, it is not worth
creating a precise point cloud.

We conducted experiments to evaluate our system andfound that the reflected waves were small and changed
irregularly for each sensor in spaces unsuitable for landing.
Using deep learning, we could determine whether a space issuitable for drone landing with 98% accuracy. We explain how
our system evaluates the surface conditions of the landing
space using ultrasonic sensors in Section II and describe theimplementations of the hardware and software in Sections III
and IV, respectively. In Section V, we present and discuss theexperimental results. In Section VI, we investigate theapplication of deep learning to the proposed system. We
conclude with a brief summary and mention future plans in
Section VII.

0

B. Directivity of Ultrasonic Sensor
Ultrasonic waves are emitted in a beam form with a certain
spread from the transducer. The spread is about 20 to 30
degrees; therefore, an ultrasonic sensor does not have pinpoint
directivity, unlike radar. An ultrasonic sensor also has
difficulty covering a wide area, unlike LiDAR.
Therefore, with our system, ultrasonic sensors are mounted
on each arm of a drone, and reflected waves are compared tofind obstacles during landing. If the distances indicated byeach ultrasonic sensor are the same, the landing site isestimated to be flat. However, if are distances are indicated tobe different, the landing space is estimated to contain an
obstacle (Fig. 3).

(a) Flat

II. EVALUATING SURFACE CONDITIONS OF LANDING SPACEUSING ULTRASONIC WAVES(b) With obstacle

A

D))))))))))))))))))))))))))))))))))))))))))))))))

D))))))))))))))))))))))))))))))))))))))))))))))))

A

B

C

))))))))))))))))))))))))))))))))))))))))))))))))

B))))))))))))))))))))))))))))))))))))))))))))))))A drone with our system can autonomously evaluate thesuitability of a landing space before landing. A drone requires
automatic landing when communication between the basestation and the drone is interrupted or when a landing signal istransmitted from the base station to the drone.
First, the drone slowly descends while sensing with theultrasonic sensors mounted underneath it. Since the measuring
distance of the ultrasonic sensors is up to 5 m, when the
distance from the ground surface or an obstacle becomes lessthan 5 m, the our system starts to evaluate surface conditions.
If the landing space is determined to be suitable, the drone willcontinue sensing and descending and then land. If the landing
space is not suitable, it will try landing somewhere else whenreturning to the take-off position.
The drone is always moving while hovering; therefore, if
its position and altitude are accurately known, a point cloud of
the landing space can in principle be created by mapping the
distance indicated by the ultrasonic sensors in a threedimensional (3D) space (Fig. 2).

X

CDistance

Distance

0

0

A

B

C

D

A

B

C

D

Figure 3. Obstacle detection with multiple ultrasonic sensors

C. Temporal Change
A conventional ultrasonic distance sensor captures theshortest reflected wave. For example, Distance A will be
detected when using such a sensor on a stepped wall, as shownin Fig. 4. Drones are constantly moving under the influence of
the wind. In addition, vegetation and thin branches move withthe wind generated by the drone. Therefore, in spaces
containing vegetation or trees, the detection distancesignificantly changes due to wind. Therefore, the surface
conditions of a landing space can be evaluated by distributing
reflected waves.
Ultrasonic sensor

Distance A

Distance B

Figure 4. Detection distance of ultrasonic sensor on stepped wall

D. Distribution of Ultrasonic Reflected Waves
Figure 5 shows the difference between a system using
conventional ultrasonic distance sensors and our system. A
conventional ultrasonic distance sensor outputs a pulse of acertain length by using a transducer. The distance is thencalculated by measuring the time until the reflected wave
returns.
With our system, however, an ultrasonic sensor outputs only
two pulses from the transducer. The pulses are then reflectedback to various spaces and return. The reflection becomes ananalog signal because the reflected waves are integrated. No
special circuit is used for integration, the reflected waves
overlap, and the signal strength increases.
If there is an obstacle on the ground, a conventional
ultrasonic distance sensor can only determine the distance tothe obstacle. However, with our system, there are two peaks:
one corresponds to the distance to the obstacle, and the othercorresponds to the distance to the ground. In fact, thedistribution becomes more complicated than that in Fig. 5, andwe can determine the surface conditions.

III. HARDWARE IMPLEMENTATION
Figure 6 shows a prototype of our system for measuring
surface conditions using ultrasonic waves. We constructed aquadcopter drone with an aluminum frame. Each transducer
was mounted at the tip of the arm, and the receivers were
mounted underneath the body.
Figure 7 shows the configuration of the system. First,
every 50 ms, the central processing unit (CPU) outputs twopulses of 40 KHz at a transistor-transistor logic (TTL) level of
5 V. Then, the pulses are amplified by an amplifier and output
from the ultrasonic transmission unit. As the two transmitted
pulses are reflected from various objects, their signal strengthchanges over time. The time series of the signal strength
obtained with the ultrasonic receiving units is amplified andintegrated by the operational amplifiers, rectified by a diode,and then converted into an analog volume change. Analog
volume changes are analog-to-digital converted by the CPU
and stored in memory. The signal strength is stored at 1 byte
per centimeter up to 5 m. Since 2000 bytes are required to store
all four channels of reflective-waves, we use PICK18F26K22
as the CPU because it has sufficient memory. The fourchannel distribution data are output via the RS-422 protocol
and sent to a personal computer via a level conversion
integrated circuit (IC) and serial-USB conversion IC.

Receivers

Transducer
Figure 6. Prototype of proposed system

Output wave of normal
ultrasonic distance sensor
Reflected wave of normal
ultrasonic distance sensor

Time to measure

IV. SOFTWARE IMPLEMENTATION

Output wave of oursystemReflected wave of oursystem[Time]

Figure 5. Comparison of system using conventional ultrasonic distancesensors and our system

We built Java software to display the distribution ofreflected ultrasound signals, as shown in Fig 8. The horizontal
axis of the graph is distance, from 0 to 500 cm. It is also
possible to zoom in from 0 to 200 cm. The horizontal axis of
the graph is the signal strength. The distribution graph isupdated in real time. If the update is too fast, the visibility will
be low, so the refresh rate can be selected from five options:50, 100, 250, 500, and 1000 ms. A dip switch for switching therefresh rate is connected to the CPU on the hardware.

2 pulls of 40 KHｚ 5 V TTL level

CPU (PIC18F26K22)

RS-422 level
conversion

AD converter
Amplifiers±8V

Amplifiers

A

B

C

D

Ultrasonictransmission
units

Ultrasonicreceiving
units

A

B

C

D

Output ultrasonic

USB serial
conversion
LaptopReflected ultrasonic

Figure 7. Configuration of proposed systemchannel. When a wooden board was placed on the deck, thesignal strengths of all four channels were almost the same (Fig.
11). Ultrasonic waves went into the gaps in the wooden deck,causing a drop in reflectivity.

Figure 7. Overview of the system
Figure 8. Display of distribution of reflected ultrasound signals withproposed system

Figure 9. Wooden deck (left) and with board placed on top (right)[Signal strength]250

V. EXPERIMENTAL RESULTS
We divided surface conditions of the landing space into
three types (suitable for landing, not suitable for landing, anddangerous for landing) and measured the distribution ofultrasonic reflected signals at multiple points.
A. Suitable for Landing
The distribution of the reflected signals of the ultrasonicwaves was measured on a wooden deck without a concreteslab. The drone hovered more than 1 m above the deck (Fig.
9). Figure 10 shows the reflected-signal distribution at thattime, in which the peak was around 100 cm for all fourchannels, but the signal intensity significantly differed for each

200

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100 120 140 160 180 200 [cm]Figure 10. Reflected-signal distribution for wooden deck[Signal strength][Signal strength]

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100 120 140 160 180 200 [cm]

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100 120 140 160 180 200 [cm]Figure 11. Results on wooden deck with wooden board

Figure 14. Reflected-signal distribution from 1.7 m above concrete slab

Hovering the drone 1 m above the concrete slab andplacing a wooden board on the slab provided almost the samereflected-signal distribution as that in Fig. 11. When thewooden board was removed, the peak was near 1 m, as shownin Fig. 12, but there was a difference in the peak height.
Because the concrete was rough due to aging, the ultrasonicwaves were irregularly reflected, and the reflected waves were
weakened. The measurement was also carried out when thedrone hovered at 0.5 m (Fig. 13) and 1.7 m (Fig. 14). At 0.5 m,
three-order reflected waves, i.e., waves reflected by the drone
and those by the concrete slab, also appeared. At 1.7 m, theshape of the waveform resembled 1 m, but the signal strength
was greatly attenuated. One characteristic of a space suitable
for landing, such as a wooden deck or concrete slab, is that thereflection-intensity peaks of the four channels align.

B. Not Suitable for Landing
We measured the reflected-signal distribution for an
uneven space and spaces with sparse and thick vegetation,
which are not suitable for landing. The uneven space waswhere animals (wild boars) had dug in search of food (Fig. 15)
and presented a risk of the drone crashing during landing.
The space with sparse vegetation was covered withvegetation of about 35 cm in height, and the space with thickvegetation was on a 40-degree slope and covered withvegetation of about 50 cm in height (Fig. 15). The drone risks
crashing when leaves become caught in its rotor.
Figures 16 and 17 show the results of the drone hovering 1
and 1.7 m above the uneven space, respectively. Since theultrasonic waves were absorbed and diffused, the reflection
intensity was very low but detectable.

[Signal strength]
250

200

150

100

50

0

20

40

60

80

100 120 140 160 180 200 [cm]Figure 12. Reflected-signal distribution for concrete slab
[Signal strength]

Figure 15. Uneven space (left), space with sparse vegetation (center), andspace with thick vegetation (left)[Signal strength]

250

250

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150

100

100

Third order reflected wave
50

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100 120 140 160 180 200 [cm]Figure 13. Reflected-signal distribution from 0.5 m above concrete slab

0

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100 120 140 160 180 200 [cm]Figure 16. Reflected-signal distribution from 1.0 m above uneven space[Signal strength]
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40

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100 120 140 160 180 200 [cm]Figure 17. Reflected-signal distribution from 1.7 m above uneven space

Figure 18 shows the reflected-signal distribution when thedrone hovered 1 m above the space with sparse vegetation. The
reflection intensity varied depending on the leaf density andthe direction of the leaf surface, but approximate flatness wasdetermined.
Figure 19 shows the reflected-signal distribution when thedrone hovered 1 m above the space with thick vegetation.
Although the signal strength from the receivers toward the topof the slope had a sharp peak, the signal strength from thereceivers toward the bottom had a gentle peak. A feature ofsurface conditions not suitable for landing, such as unevennessand vegetation, is that the reflection intensity of the fourchannels varies irregularly.

C. Dangerous for Landing
We measured the reflected-signal distribution where there
was a stone and rock. In such spaces, the propellers, legs, orarms risk coming into contact with the stones and rocks,
causing the drone to lose its balance and crash.
The stone was about 15 cm high, and the drone hovered 1
m above the ground. Figure 21 shows the reflected-signal
distribution just above the stone. Although the space aroundthe stone had sparse vegetation and was possible to land in, it
was difficult to distinguish between the stone and vegetation
due to the large amount of ultrasound diffusion and low
reflection intensity (Fig. 21a). However, when viewed inchronological order, peaks were occasionally generated
around 80 cm, so stones can possibly be identified by looking
at the temporal change (Fig. 21b).
The rock was about 70 cm tall, and the drone hovered 1 m
above the ground and thus 30 cm above the rock. Figure 22
shows the reflected-signal distribution, with a peak around 30
cm and the rock being recognized. There was also a peak near
1 m, and the ground was also recognized.

[Signal strength]

Figure 20. Stone (left) and rock (right)

250(a) Difficult to distinguish between stone and vegetation[Signal strength]

200

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Figure 18. Reflected-signal distribution from 1.0 m above space with sparsevegetation0[Signal strength](b) Small peak at 60 cm

250[Signal strength]

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100

Reflected from stone

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100 120 140 160 180 200 [cm]Figure 19. Reflected-signal distribution 1.0 m above space with thickvegetation

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100 120 140 160 180 200 [cm]Figure 21. Reflected-signal distribution for stone and drone hovering at 1.0 m

(a) Big peak at 100 cm[Signal strength]•

Creating basic reflected waves
All collected reflected waves, as mentioned in Section
V, are divided into basic reflected waves. A basic
reflected wave is obtained by deleting a portion wherethe value of the original reflected wave is 0 anddividing the distribution into several distributions.
Fifty basic reflected waves were extracted.

•

Superposition of the basic reflected waves
Reflected waves are artificially generated by shifting
the positions of the basic reflected waves andsuperimposing them. An artificial reflected wave hasa vertical axis from 0 to 255 cm and a horizontal axis
from 0 to 500 cm. The number of superimposed basic
reflected waves is randomly determined between one
and five. The shift in the distance direction is
randomly determined between -250 and +250 cm, but
excludes the cases in which a part of the fundamental
reflected wave is 0 or less or the entire fundamental
reflected wave exceeds 500 cm. The size of thefundamental reflected wave varies randomly in 0.1
steps from 0.1 to 3 times. If the vertical axis exceeds
255 as a result of the superposition, it is excluded.

•

Determine the generated reflected wave
By using the method described in VI A, the generated
reflected waves are determined as suitable or not forlanding and labeled. We generated one million
reflected waves suitable for landing and one million
reflected waves not suitable for landing.

250

Reflected from rock
200

150

Reflected from ground
100

50

80

100 120 140 160 180 200 [cm](b) Small peak at 100 cm[Signal strength]
250

200

Reflected from rock
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100

Reflected from ground50

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100 120 140 160 180 200 [cm]Figure 22. Reflected-signal distribution for rock and drone hovering at 1.0 m

VI. PROPOSED SYSTEM USING DEEP LEARNING
A simulation experiment was conducted to determine
whether a space’s suitability for landing can be estimated byusing deep learning.
A. Determining Surface Conditions for Landing
When the following surface conditions are satisfied, a spaceis determined to be suitable for landing.
••

Flatness of landing space
Except in a space where there are many irregularreflections of ultrasonic waves, such as uneven land, amaximum peak occurs at the position of the ground. If
the four maximum peaks are almost at the same
position (within 3 cm), the landing space isdetermined to be flat.
No obstacles
Obstacles such as stones and rocks are difficult torecognize, as shown in Figs. 20 and 21. Therefore, anyreflection at a distance shorter than the four maximum
peaks is regarded as an obstacle.

B. Data Augmentation
The data acquired thus far using the drone are limited. Also,
carrying out many measurements is costly. Therefore, thelearning data is augmented by using the following data.

C. Learning and Evaluating by Deep Learning
We used a multi-layer perceptron (MLP) for learning therelationship between the generated reflected waves and thelabel. The input layer was a 2000-dimensional layer, i.e., 500dimensional reflected wave from each of the four sensors.
There were four intermediate layers, each having 1,000 unitsof perceptron (Fig. 23). We used 90% of the data as learning
data and 10% as evaluation data. The accuracy of the data was
0.98; most of the incorrect data were when the maximum
peaks of the four distributions were slightly longer than 3 cm.
Input
layer

……………...

60（500-dimensionalreflected wave x 4）

40

2000-dimensional layer

20

Hiddenlayer 1

Hiddenlayer 3

Hiddenlayer 4nOutputlayer
Suitable for landing
Not suitable forlanding……..

0……………...

60……………...

40……………...

20

……..

0

Figure 23. MLP for estimating surface conditions of landing spaceVII. CONCLUSION
We proposed a system to evaluate the surface conditions ofa space for a drone to land using ultrasonic sensors attachedto each arm of the drone. The ultrasonic transmitting unit
outputs two pulses of ultrasonic waves, and the receiving unit

receives the reflected waves. By integrating the received
waves by using an operational amplifier and rectifying themby using a diode, a circuit is created that causes a peak at an
object or on the ground.
The results indicate that, in spaces suitable for landing, thefour peaks were all in the same place and that the surface
flatness could be evaluated. In spaces not suitable for landing,the signals from the four sensors had irregular movements. In
spaces dangerous for landing, there was another peak besides
the ground. From results of a simulation experiment, oursystem could determine whether a space was suitable forlanding with an accuracy of 0.98 by using deep learning.
We plan to construct an emergency landing system for asmall lightweight drone [to use for detailed evaluations
outdoors.
ACKNOWLEDGMENTS
This work was supported by the Japan Society for the
Promotion of Science (JSPS KAKENHI Grant Number
17K19972).
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