Motor Speed Control toward Wall Surface Angle based on HC-SR04

Ultrasonic Sensor

Peri Turnip

, Erwin Sitompul

, Bambang Mukti Wibawa

, Agus Trisanto

, and Arjon Turnip

Computer Engineering, Politeknik Pajajaran, Bandung,Indonesia

Study Program of Electrical Engineering, Faculty of Engineering, President University, Indonesia

Department of Electrical Engineering,Universitas Padjajaran, Bandung, Indonesia

Keywords: Brain-Controlled Wheelchair, Control Motor DC with PWM.

Abstract: Brain-controlled wheelchair is an assisting device for patients with motor disabilities controlled by brain

waves. The convenience and security of users is the focus of the development of brain-controlled

wheelchairs. In this final task was designed dc motor speed control system using pulse width modulation

(PWM) method on a different surface slope using ultrasonic sensors. With this method it is expected that the

wheelchair can move at different speeds under certain surface conditions. This way the concept of security

of the user will be fulfilled for the future. The detection distance of the obstacle object is influenced by the

intensity of humidity, namely the drier the place gives more accurate results in its measurement. Research

on motor speed controllers can use other control methods to ensure that motor speed is stable on flat and

sloping surfaces, and to improve processing speed and object detection accuracy.

1 INTRODUCTION

Wheelchairs are usually used to help move people

with partial or total paralysis. Conventional or joistic

wheelchairs whose movements must be assisted by

other people or hand movements have not been able

to help people who are completely paralyzed to

move or move independently. Thus, for people with

total paralysis, a wheelchair that can be moved

through the mind is needed. The majority of people

with total paralysis can still think well. This brain

signal activity will then be used to move the motor

instead of joistic. The development of this

technology is supported by the development of

biosignal science which is able to identify and

classify brain signals for specific functions.

The ability to move freely is the desire and need

of each individual. Especially for people with

disabilities who have limited space. Not all persons

with disabilities can use their own wheelchair to

travel, therefore with the help of technology a Smart

Wheelchair or Electric Wheelchair is developed

based on control using physiological brain waves.

Physiological brain waves can be used to control

wheelchair movement (such as forward, stop, turn

right, or turn left) by recording and analyzing brain

biosignals. Electroencephalogram (EEG) is used to

record biosignals from the brain which can then be

used to run a wheelchair. In this case, control is

carried out of the user's brain signal activity

supported by several instruments such as sensors.

EEG is a device that captures the activity of

bioelectrical signals recorded from electrodes on the

scalp. In medicine, EEG is used to diagnose diseases

such as Alzheimer's and epilepsy. In this case, EEG

can be used as a controller that utilizes these

bioelectrical signals. The processing of data obtained

from the EEG makes it possible to group one's

thoughts in the form of waves. This can be used as

information for controllers, by adjusting the data that

has been trained to control wheelchair movement.

This wheelchair with EEG system cannot reduce

and increase the motor speed. EEG based signals can

only be used for certain movements such as forward,

backward, turn, stop. Therefore, in developing the

Brain-Controlled Wheelchair system, several

additional sensors are needed to obtain certain

information such as detecting obstacles, road slopes,

and others. Several studies related to brain-

wheelchair development have been carried out

[Turnip, A et al 2015; Turnip, M et al 2015].

In a previous study on smart wheelchairs,

entitled Ultrasonic Tethering to Enable Side-by-Side

Following for Powered Wheelchairs, tested and built

630

Turnip, P., Sitompul, E., Wibawa, B., Trisanto, A. and Turnip, A.

Motor Speed Control toward Wall Surface Angle based on HC-SR04 Ultrasonic Sensor.

DOI: 10.5220/0010370900003051

In Proceedings of the International Conference on Culture Heritage, Education, Sustainable Tourism, and Innovation Technologies (CESIT 2020), pages 630-637

ISBN: 978-989-758-501-2

a system using an ultrasonic sensor where a

wheelchair can detect someone to be used as a guide

in moving (Pingali et al., 2019) ). Another study

using ultrasonic sensors in wheelchairs is "Low cost

sensor network for obstacle avoidance in share-

controlled smart wheelchairs under daily scenarios"

by (Pu et al., 2018). In this journal, four cheap

ultrasonic sensors are used on the underside of the

front of the wheelchair to detect obstacles in front of

and below the wheelchair. A fuzzy logic navigation

controller implemented in hardware for an electric

wheelchair by (Rojas et al., 2018; Turnip et al, 2015;

Turnip et al, 2015) uses ultrasonic sensors with the

help of a fuzzy logic algorithm in determining

direction decisions on wheelchair movement. Smart

Navigation and Control System for Electric

Wheelchair” by (Dakhilallah et al., 2019) also uses

ultrasonic sensors as an aid in navigation. In the

paper, it is explained that in addition to ultrasoic

sensors, a controller using a joystick is also built as a

navigation aid. Smart Autonomous Wheelchair

Controlled by Voice Commands-Aided by Tracking

System explains how to control a wheelchair using

voice commands and also uses a tracking system to

find out the position of the wheelchair itself

(Alkhalid & Oleiwi, 2019). Autonomous Wheelchair

with a Smart Driving Mode and a Wi-Fi Positioning

System discusses the integration design using GPS

and wifi as a navigation tool while security uses

ultrasonic sensors and IR sensors (Manjunath, 2018;

Turnip et al, 2015).

Controlled Wheelchair System Based on

Gyroscope Sensor for Disabled Patients examines an

electric wheelchair that is controlled using motion

using an ultrasonic sensor as a safety device from

obstacles (Al-Neami & Ahmed, 2018). In this study,

it was also explained that the head movement data

used were obtained from a gyroscope sensor

mounted on the user's head. Voice Recognition

based on Intelligent Wheelchair and GPS Tracking

System explains that a wheelchair can be operated

by voice command by using firebase to control the

direction of the wheelchair for both forward and

backward (Aktar et al., 2019). Autonomous

wheelchair under a predefined environment

implements an electric wheelchair operating system

by using a keypad as a controller in movement and

using an IR sensor and also an Ultrasonic sensor as

an obstacle detection system (Roslan et al., 2017).

Smart Wheelchairs: Using Robotics to Bridge the

Gap between Prototypes and Cost-effective Set-ups

designed a wheelchair with automatic and manual

control, where manual control uses a joystick as the

controller and also uses an Ultrasonic Sensor as a

detecting obstacle and obstacle course (Aquilina et

al. al., 2019).

From all the journals that have been mentioned

above, there are many studies taking references on

how best ultrasonic sensors can work properly by

combining them with several other sensor devices.

However, in this study the ultrasonic sensor is used

in a different way, namely to measure the angle of

the wall surface or other obstacle. Furthermore, the

tilt information will be used to adjust the motor

rotation speed. Ultrasonic sensor (HC-SR04) is used

as a tool to calculate the slope angle to support the

brainsignal-based wheelchair motor controller from

the EEG system.

A motor speed control system with a pulse width

modulation (PWM) method on a different surface

slope using an ultrasonic sensor is designed. With

this method, it is expected that wheelchairs can

move at different speeds in certain surface

conditions. However, this system still has limitations

where the measurement results obtained are still less

precise.

2 THEORY

2.1 Brain-controlled Wheelchair

Brain-Controlled Wheelchair is a technology that

combines a wheelchair with a Brain-Computer

Interface (BCI) with the aim of helping wheelchair

control to make it easier for people with motor

disorders. BCI can communicate directly between

the computer and the brain. This allows wheelchair

surgery to use commands from the brain's

physiological signals while thinking. BCI is able to

represent the user's thoughts into controlling

wheelchair movement according to the user's wishes.

2.2 Ultrasonic Sensor HC-SR04

Ultrasonic sensor HC-SR04 is a sensor measuring

distance based on ultrasonic waves with a working

principle: the emitted ultrasonic waves are then

received back by the sensor receiver itself, then

detects any object in front of it in the form of a solid

object or a barrier. The detection range is 2-500cm,

the detection angle is 15 degrees, and can be

connected directly to the Arduino microcontroller

input / output (Saputra, 2013). The HC-SR04 has

two main components as a constituent in the form of

a transmitter emitting ultrasonic waves with a

frequency of 40 KHz and the receiver capturing the

Motor Speed Control toward Wall Surface Angle based on HC-SR04 Ultrasonic Sensor

631

results of ultrasonic wave reflections (Setyawan et

al., 2018).

2.3 Arduino UNO R3

Arduino UNO R3 is a microcontroller development

board based on the ATmega328P chip. Arduino

UNO has 14 gigital pins input / output pins (I / O, of

which 14 pins can be used as Pulse Wiidth

Modulation (PWM) output, including pins 0 to 13),

6 analog input pins, using a 16 Mhz crystal,

including pin A0 to A5, USB connection, power

jack, ICSP header and reset button. All of these pins

are required to support a microcontroller circuit. The

worst possibility is just damage to the ATMega328

chip, but it can be replaced easily and relatively

cheaply. Since the initial launch until now, Uno has

developed into a Revised 3 version or commonly

written as REV 3 or R3. Arduino IDE software,

which can be installed on Windows as well as Mac

and Linux, functions as a software that helps you

enter (upload) programs to the ATMega328 chip

easily (Hasriyani & H, 2018).

Integrated Development Environment (IDE) is a

software used to develop microcontroller

applications starting from writing source programs,

compiling, uploading compilation results and testing

in serial terminals. Arduino can be run on computers

with various platforms because it is supported or

based on Java. The source program for

microcontroller applications is C / C ++ and can be

combined with assembly.

2.4 Pulse with Modulation

One way to adjust the rotational speed of a dc motor

is by means of pulse width modulation (PWM).

Pulse width modulation (PWM) is a way of

manipulating the pulse width in one. PWM signals

generally have a fixed basic amplitude and

frequency, but have varying pulse widths. The PWM

Pulse Width is directly proportional to the amplitude

of the original unmodulated signal. This means that

the PWM signal has a fixed wave frequency but the

duty cycle varies (from 0% to 100%) (Hasriyani &

H, 2018). Duty cycle is the percentage of high pulse

length in one signal period. When the duty cycle is

0% or the signal is fully low, the value of the voltage

output is 0V. When the duty cycle is 100% or the

signal high is full, the voltage output is 5V.

The PWM that can be generated by Arduino has

a data allocation of 8bit, or has a variation of

parameter value changes ranging from 0 - 255, a

change in value that represents the duty cycle of 0 -

100% of the PWM output. To set the duty cycle

value on Arduino, use the analogWrite function

([Pin number], [value]). If the duty cycle is to 0%

and 100%, then set the parameter values to be 0 and

255, respectively. So the duty cycle is 50%, meaning

that the parameter value that must be set is 127.

PWM graph can be seen as in Figure 1.

Figure 1: PWM graph.

3 DESIGN AND DEVELOPMENT

Ultrasonic sensor signal processing aims to

determine the accurate distance between a

wheelchair and a wall in an inclined or non-vertical

position. The signal that is obtained is data from the

sensor on objects that are obstructions in front, side

and back of the wheelchair. The design of a speed

control system for the wall surface slope using an

Ultrasonic sensor is illustrated in Figure 2. The

system workflow starts from reading the distance

data on the Ultrasonic sensor. The distance data

from the two sensors as a reference for flat and tilted

surfaces is processed by Arduino UNO R3.

Furthermore, Arduino will be given a program with

an if condition, if sensor a is the same as sensor b

then it is assumed that the sensor detects a flat

surface, otherwise if one of the sensors reads the

distance is less then it will be detected as a sloping

surface. PWM control provides control action on the

motor based on the distance obtained where if the

surface detected is flat, the motor will reduce the

speed according to the predetermined speed,

conversely if the surface is detected is tilt, the motor

CESIT 2020 - International Conference on Culture Heritage, Education, Sustainable Tourism, and Innovation Technologies

632

speed will be lowered according to the angular

conditions obtained in the calculation by the system.

Figure 2: Sheme of velocity control system for wall

surface slope using Ultrasonic sensor.

3.1 Motor Speed Control Design

The ultrasonic sensor added to the wheelchair

system is a device used to read the distance of an

object or object surface that will be traversed by a

brain-controlled wheelchair. Furthermore, the

motion controller is used to determine the precision

of an instrumentation system with the characteristics

due to good feedback on the system. Motion

Controller is used as a speed controller by providing

control action on the DC motor based on the

distance value obtained. Furthermore, the DC motor

will provide a speed value that is close to the desired

value in the form of a Set Point value.

3.2 Obstacle Detection System Design

Motion Control usually refers to a system with

accurate position, speed, torque capability that

operates in open or closed loop mode. The open-

loop drive sends a Movement command to the DC

motor, but receives no information about the result.

The closed loop system has a feedback device on the

motor shaft to verify or adjust the resulting motion.

The initial stage begins with data acquisition

from TrigPin by ultrasonic sensors (Figure 3). The

calculation process is carried out at the recorded

wavelength and the system will divide the wave

according to the equation used to convert the

wavelength into a measurable distance. In

processing signals from ultrasonic sensors, the

concept of median-filter is used as a reference so

that the data obtained has less noise and good

quality. The Median-Filter has a characteristic: it

only uses some of the data obtained previously and

is then processed to produce an accurate value by

taking the middle value of some of the data collected

and has sufficient accuracy because it always

performs calculations when the sensor reads new

data. The filter aims to minimize reading errors from

the ultrasonic sensor. The process of measuring

angles is obtained with several conditions that must

be met in order to obtain accurate and consistent

results. The calculation of angles and distances is

obtained from equations (1) and (2). The calculation

process is expressed in Figure 4. The wheelchair

kinematic model is used as a reference in

calculations to determine the wheel speed whether

the wheel rotates more to the left or right.

Figure 3: Recording and division process of recorded

wavelengths to get the closest wheelchair distance with the

wall.

𝜃tan





𝑎𝑏

𝐿𝑢



(1)

where, θ is the angle formed from the results of

calculations that have been done, Lu is the distance

between the ultrasonic sensor a and the ultrasonic

sensor b, a is the distance between the ultrasonic

sensor a and the wall, and b is the distance between

the ultrasonic sensor b and the wall.

ℎ



𝑎𝑏



cos𝜃

(2)

Motor Speed Control toward Wall Surface Angle based on HC-SR04 Ultrasonic Sensor

633

where θ is the angle formed from the results of

the calculations that have been done, h is the actual

distance between the wheelchair and the wall.

The following equation (3) is used to convert the

angle value into radians so that it can be recognized

by Arduino;

𝑛𝑁

𝜋

(3)

where, n is the value of the angle of the degree you

want to find, N is the radian value that we know

beforehand, and is a value that contains 3.14.

Figure 4: Calculation of the distance of a wheelchair to a

sloping wall.

4 RESULTS AND DISCUSSIONS

Tool testing is carried out on hardware by obtaining

data using the serial monitor from the Arduino IDE.

The data obtained include: distance data read by the

ultrasonic sensor by testing the distance, angle and

velocity. The actual distance was measured as

comparative data for the results from the system.

Ultrasonic sensor test data aims to determine how

much influence the distance between the object and

the rotation speed of the DC motor wheel.

The waves from the ultrasonic sensor are sent by

setting the pin in the input state or as output on the

microcontroller. The distance was obtained by

calculating the speed of propagation of the wave

against the time difference between the transmitter

of the wave by the transmitter and the time when the

reflected wave is received by the receiver. The

received distance is processed again to get the

results from the calculations between several sensors

installed. The data is processed to obtain accurate

angles and distance calculations. The following is a

snippet of program code for the change process from

the distance received by the sensor to be converted

into the desired distance (Figure 4). Ba is a variable

that is made to accommodate the value of the

reduction between sensors a and b then it is divided

by the distance between the two sensors. Su is a

variable created to accommodate the tan-1 value of

Ba; Cs is a variable created to accommodate the cos

value of Su; h is the ideal distance that you are

looking for or you can also call it the setpoint. After

fulfilling the conditions described in the program

code, then the wheelchair speed is from the distance

that has been obtained. Figures 5 and 6 are the

program code for distance and the speed control of a

dc motor.

Figure 5: Program code snippets for distance calculation.

Figure 6: Code for setting motor speed.

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Object detection testing using two ultrasonic

sensors was carried out to determine the success in

the detection of angles and distances with the

processing process that had been designed. The

simulation calculation results to get the closest

distance to the wheelchair and the change in the

motor rotation speed to the change in angle can be

seen as in Table 1 where



is the change in angle, a

and b are the distance of each sensor on the wall

(cm), h is the calculated closest distance v is the

rotational speed of the motor due to the change in

distance h, and Lu is the constant distance between

sensors.

Table 1: Calcualtion resuts on simulation with disferent

angels.



a b h v Lu

75 50 5 7.1 0 12

70 50 17 11.45 0 12

65 50 25 16.22 0 12

60 50 29 20 0 12

55 50 33 24 0 12

50 50 36 28 32 12

45 50 38 31 32 12

40 50 40 35 32 12

35 50 41.5 37 40 12

30 50 43 40 40 12

25 50 44.5 42 50 12

The results of manual calculations in Table 1

show that when the h value is less than 28 cm, the v

value automatically becomes zero, which means that

the wheel rotation stops. In this case the wheelchair

will wait for information from other sensors such as

cameras or brain signals to decide whether to stop or

turn (left or right) or reverse. When the distance or h

value is greater than or equal to 28 cm, the wheel

rotation becomes 28 rad / s and will continue to

increase as the h value is greater. If the wheelchair

moves towards the wall at a certain angle, the sensor

will calculate the closest distance before the

wheelchair hits the wall. Because the angle



is less

than 90 degrees, the distance between the wheelchair

and the wall will differ between sensors, so it is

necessary to estimate it.

Figure 7 shows the test results displayed using a

serial monitor in realtime where the distance and

speed generated on the monitor screen work in

accordance with the conditions obtained. Figure 8 is

the print result of the serial plotter where it can be

seen that the ultrasonic sensor distance changes

quickly. These changes occur because of the

disconnection of sensors in detecting objects. With

the help of filters and averaging this problem can be

solved. If this result is used directly to the motor

rotation it will result in unstable wheelchair

movement. Further development related to this

problem needs to be done, namely by increasing the

ability of filters and estimators. In this experiment,

the results of this problem can still be resolved

considering that testing is still limited. However, if

applied with a wheelchair on a varied track, it is

assumed that the stability will decrease.

Figure 7: Realtime Test Using Serial Monitor.

Figure 8: Serial plotter display.

5 CONCLUSIONS

The object detection system based on the ultrasonic

sensor with a direction at a certain slope is able to

recognize obstacles in the form of walls in realtime.

A filtering system to minimize unwanted values due

to sensor limitations as well as changes in

wheelchair direction has been developed with

accurate results. The detection distance of the

obstacle object is influenced by the intensity of

humidity, namely the drier the place gives more

accurate results. Research on motor speed

Motor Speed Control toward Wall Surface Angle based on HC-SR04 Ultrasonic Sensor

635

controllers can use other control methods to ensure

that motor speed is stable on flat and sloping

surfaces, and to improve processing speed and

object detection accuracy. The offline and realtime

test results show fairly accurate results where a

wheelchair is able to detect objects in the form of

walls at an angle of less than 90 degrees.

Incorporation of additional sensors such as an

ultrasonic sensor to detect paths before the IMU

sensor detects surface slope is necessary. The motor

used must be equipped with a speed reading so that

the accuracy of the speed reading can be better.

ACKNOWLEDGEMENTS

This research was supported by Technical

Implementation Unit for Instrumentation

Development, Indonesian Institute of Sciences,

Department of Electrical Engineering, Universitas

Padjadjaran, and Toba Research Center, Indonesia.

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