CHARASTERISTICS OF SONAR RANGE SENSOR SRF05

.A. Wickramasooriya, G. Hamilan, S.I.L. Jayawardena, L, W.M.D.L.W. Wijemanne, S.R. Munasinghe

Department of Electronics and Telecommunication Engineering

University of Moratuwa 10400, Sri Lanka

wathswijemanne@gmail.com

ABSTRACT

Sonar sensors are widely used in obstacle detection in

mobile robotic applications and certain limitations of these

sensors usually become the reason for failures in these

applications. Therefore, a thorough analysis of the actual

behavior of sonar sensors is required. This research studied

Devantech SRF05 sonar sensor, which is the most common

sonar sensor in practice. We have discovered its practical

beam pattern in 3-dimension (3D), optimum trigger

frequency, and maximum angle of detection.

Index Terms — Beam pattern, SRF05, sonar sensor, trigger

frequency, filtering algorithm.

1. INTRODUCTION

In mobile robotic applications sonar sensors are widely used

in obstacle detection and map building. However without a

proper knowledge of the sensor characteristics, and

limitations, it is difficult to achieve desired performance.

Here we present those limitations and techniques to

overcome them. We have used Devantech SRF05 sonar

sensor, which is widely used mainly due to range and low

cost. The main problem of sonar sensors is the lack of

knowledge of the 3D beam pattern of the sensor. The

provided data by the manufacturer [2] is only very general.

The trigger frequency of the sensor is also critical. We also

found that surface texture, audibility angles of the surface,

area of surface, and location of acoustic features on a

surface are also important facts for obstacle detection using

sonar sensors.

2. ANALYSIS OF THE BEAM PATTERN OF

DEVANTECH SRF05

(a)

(b)

Fig.2 (a) SRF05 sonar sensor, (b) Beam Pattern

Figure 2 shows SRF05 sonar sensor and its beam pattern.

We analyzed the behavior of the single sensor in a vacant

room, where echo was only received from a specific object.

We used a 25 × 25 cm plastic plate as the standard object

and gathered readings for different orientations. Figure 2

shows the experimental arrangement in that the object was

moved along horizontal and vertical straight lines in x-y

plane at several distances in z-axis.

Fig.2 Experimental Arrangement

x

z

y

h

z

Object

2.1 Accurate Beam Pattern

We approached in different ways to check the detectable

area of the sensor. First we measured the horizontal and

vertical width of the sensor beam at particular distances as

shown in Fig. 3

Fig. 3. Test to discover the beam pattern of SRF05 sensor

Fig. 4a and Fig. 4b shows the readings taken at distances of

d=z=100cm and d=z=300m. The data were collected from

d=z=0cm to d=z=400cm meters at 25cm separations. From

that we were able to identify two separate areas according to

the probability of detection.

Fig. 4a. Test data at the line {x, y=0, z=100}

Fig. 4b. Test data at the line {x, y=0, z=300}

Based on these results, we identified a high probability area

of detection (HPAD) and a low probability area of detection

(LPAD) using 0.7 probability of detection on the straight

line of {x, y=0, z=100} as shown in Fig. 5

Fig.2.4c. Area Seperation According to the Probability of

Detection at the line- {x, y=0, z=100}

Then we moved the object in z direction slowly (i.e. parallel

to the beam axis) and measure the readings and crossing

points of the beam. From that we could draw the beam

pattern of the sonar sensor for the detection of 25x25cm

object facing direct to the sensor.

We have placed the sensor horizontally, vertically and 45

degrees inclined as in Fig.2.5. The standard object was

moved in z-direction away from the sensor. We have taken

reading three times from the sensor with five sensors. It was

found that there were no major differences in the beam

pattern.

From the observation we could justify the above said

separation of the beam pattern into two different areas

depending on the probability of receiving an echo. From the

averaged value we have drawn the beam pattern.

Fig.2.5. Accurate Beam pattern of SRF05 sonar

Graphs included in Fig 2.6a, 2.6b and 2.6c below visualize

the test data received in this testing.

LPAD HPADD

x z

y

Fig.2.6a. Object moving along the line- {z, y=0, x=-10}

Fig.2.6b. Object moving along the line- {z, x=0, y=40}

Fig.2.6c. Object moving along the line-

{z, x=60×cos60, y=60×cos60}

Fig.2.7. Data Fluctuations

2.1.1 Accurate Beam Pattern - Results

The resulted 3-D beam pattern is illustrated below in several

2-D planes for the ease of the understanding. The planes are

named according to the axial representation shown in

Fig.2.2.

Fig.2.9. Beam pattern along the yz plane at x=-20

Fig.2.10. Beam pattern along the yz plane at x=-10

Fig.2.13. Beam pattern along the yz plane at x=10

Fig.2.12. Beam pattern along the yz plane at x=20

Fig.2.15. Beam pattern along the xz plane at y=-20

Fig.2.16. Beam pattern along the xz plane at y=-10

Fig.2.19. Beam pattern along the xz plane at y=10

Fig.2.18. Beam pattern along the xz plane at y=20

As described previously the area where we keep an object

and the probability of receiving echo is more than 0.7 is

defined as the high probability area of detection (HPAD).

This is covered along the beam axis. The area where object

is placed and the probability of receiving echo is less than

0.7 is defined as the low probability area of detection

(LPAD). This also depends on the size of the object. We

defined these areas according to our standard size object.

When the size of the object is even larger the HPAD may

expand into the LPAD.

Test 1 Results

Fig.2.20a and 2.20b illustrates data the variation of distance

when the standard object was moved at different distances

from the sensor along x – direction.

Fig.2.20a. Reading variation at along the line {x, y=0, z=368}

Fig.2.20b. Reading variation along the line {x, y=0, z=151}

2.3 Triggering Frequency

The SRF05 sensor only detects the first echo coming from

the object. We supply a short 10µs pulse to the trigger input

to start the ranging. The SRF05 will send out an 8 cycle

burst of ultrasound at 40 kHz and raise its echo line high. It

then listens for an echo, and as soon as it detects one it

lowers the echo line again. The echo line is therefore a pulse

whose width is proportional to the distance to the object. By

timing the pulse it is possible to calculate the distance. If

nothing is detected then the SRF05 will lower its echo line

after about 30ms.

The area power density (W/m2) of the echo declines as it

travels. We needed to set the triggering time of sensor such

that the power of the previous burst has declined such that it

will not affect the next trigger.

Fig.2.21. Effect of Multiple Echoes – Object at the point-

{x=0, y=0, z=200}

2.3.1 False Echoes

Multiple echoes are the result of an environment with many

objects. The sonar burst from sensor is spread to the

environment and may generate echoes from several objects

around the sensor. But the SRF05 is designed only to detect

the first echo. Therefore other echoes may affect when the

sensor is triggered again giving a false reading. Fig.2.21

clearly illustrates this scenario. So to avoid these false

readings sufficient amount of time should be left before

triggering the sensor again.

The minimum time the wave travels with echo power higher

than the threshold value was found to be 35ms. When we

have set the triggering time as greater than this value, the

output was stable and is shown in Fig.2.22.

Fig.2.22. Triggering time 35 ms Object at the point-

{x=0, y=0, z=300}

We have developed an algorithm in order to overcome the

problem of multiple echoes and false readings which is

described in section 2.5.

2.5 Generalized Filtering Algorithm

After a thorough analysis of this sensor, we were able to

develop a general filtering algorithm to eliminate the

uncertainty of the sensor data. The functionality of the

filtering algorithm can be summarized as follows.

Variables defined (assuming data is coming from m number

of sensors at n number of scans):

Input data at kth

scan� ri (k); i= 1,2,3,…m

k=t=f(∆T),∆T=1/ft

Output data array � Rm

Intimidate data array � Fm

Pointer array for one set of data � fm

2D array to indicate whole set of data � Pm, n

Pre defined range value � d1

A. If ri (k) = 0

Pi (k) = 0

Ri (k) =

B. Else

a. If Ri (k-1)- d1 < ri (k) < Ri (k-1) + d1

Pi (k) = 1

Ri (k) =( Fi (k-1) + ri (k) ) / 2

b. Else

Pi (k) = 0

I. If fi(k-1) = 1

fi(k) = 0

If Fi (k-1)- d1 < ri (k) < Fi (k-1) + d1

Ri (k) =( Fi (k-1) + ri (k) ) / 2

II. Else

fi(k) = 1

Fi(k) = ri(k)

Ri(k) = Ri(k-1)

Same process is executed for the scans, k+1, k+2, …

So the output of the algorithm is smoothened one set of data

which is reasonably able to represent all the n scans. Since it

False echoes

has correlated several set of data the filter has successfully

tracked the object for a period of time.

This filtering algorithm is shown in Fig. 2.23.

Fig.2.23. Flow Chart of the Filtering Algorithm

Behavior of raw data and filtered data is demonstrated in our

GUI using two graphs. Two axes of the graph indicate the

scanning time & distance, and at the same time 9 colors are

used to represent nine sensors. Fig.2.24. shows the behavior

of one sensor data, raw data and filtered data, at an instance

where the module is detecting an object at the distances of 2

m.

Fig.2.24. Graphs of the Raw Data and Filtered Data for one

sensor

3. CONCLUSION

This work provides the necessary guidelines for the

optimum usage of the most frequently used sonar range

finder SRF05.

Sonar beam pattern in 3D has been determined accurately

which would limit the failures due to the assumptions about

the beam pattern.

Best triggering frequency has been found to be 33ms. This

allows to trigger the sensor at a faster rate as required by the

application.

The ideas presented under sonar wave propagation and

multiple echoes provide a better insight to the behavior of

these sensors and would definitely be advantageous in

usage. The maximum angle of detection also provides the

necessary information in using these sensors to detect

inclined objects and to avoid missing the important data.

If these guidelines are adhered, there would be a much

better possibility to obtain the maximum usage of range

finder SRF05 sonar.

4. REFFERENCE

[1] Joanne walker, “Intelligent Robotics”

[2] SRF05 Technical Specifications

[3] Daniel .J. Dailey, Patricia Harn, and Po-Jung Lin “ITS

Data fusion,” Washington State Transportation

Commission, Washington, April 1996.

Uncertain

behavior

of the data

Smoothed

data