gps based navigation and collision avoidance system using ultrasonic sensors and image processing...

Asher Rahman, et al International Journal of Computer and Electronics Research [Volume 3, Issue 4, August 2014]

©http://ijcer.org e- ISSN: 2278-5795 p- ISSN: 2320-9348 95

GPS based Navigation and Collision Avoidance

System using Ultrasonic Sensors and Image Processing

for Autonomous Vehicle

Asher Rahman1, Muhammad Farhan Aslam

2, Hassan Ejaz

3

[email protected],

[email protected],

[email protected]

1,2,3Student, National University of Sciences and Technology (NUST), Karachi, Pakistan

Abstract—The purpose of this work is to design a small

electric autonomous vehicle which incorporates GPS-based

navigation and utilizes ultrasonic sensors and image processing

for collision avoidance. The deliverable product is a self-

navigating unmanned vehicle which drives to user-defined

destination coordinates which are transmitted wirelessly to the

vehicle. GPS and compass modules are used for navigation of

the vehicle while an effective collision avoidance system is also

developed. The position of obstacles are detected and collision is

avoided using image processing (via camera) for the vehicle’s

front side and ultrasonic sensors for its other three sides. AVR

microcontrollers control and interface with motors, modules

and sensors, while for image processing purposes, a Linux

based Single Board Computer (SBC) is employed. The

developed system can be used in a variety of applications

including scientific research, surveillance, and navigation to

places unsafe for humans.

Keywords: Collision, SBC, Ultrasonic, Vehicle.

I. INTRODUCTION

The modern automobile is an important innovation which

casts a direct impact on the everyday lives of humans. It has

allowed man to travel long distances in a relatively short

period of time and has undergone developments from time to

time to enhance its capabilities and improve its performance

in terms of speed, maneuverability, aesthetic look, design,

comfort and features. The development of autonomous

vehicles is a fast growing field within the modern

automobile sector. Working prototypes of autonomous

vehicles have been introduced by various research

organizations and companies, which allow vehicle to drive to

a destination without human input. Such vehicles have

numerous applications, ranging from commercial use on

roads, scientific research and monitoring of environment

which is unsafe or unreachable for humans, and for

surveillance and espionage purposes. It is essential that such

vehicles navigate to the precise user-defined destination

while taking shortest path. It is also imperative for any

autonomous vehicle to effectively avoid collision and

prevent accidents so that it performs its functions and drives

to the destination with minimum disturbance.

In this paper, we present the design and implementation of

an autonomous vehicle. GPS and compass modules keep

track of the current position and heading respectively, and

help navigate vehicle towards the destination point. The

destination point is entered by user and transmitted

wirelessly to the vehicle via a Radio Frequency (RF)

module. The second RF module i.e. the one onboard vehicle

receives these coordinates and also sends current GPS

coordinates of the vehicle after regular intervals to the user.

Alternative solutions involved using GLONASS, Galileo or

Beidou based navigation system. However, we did not prefer

those because GPS is the most popular and widely used

system and we were well acquainted with it. Furthermore,

GPS modules are widely available unlike modules of other

navigation systems. Moreover, navigation systems such as

Galileo and Beidou are in developmental phase and have

limited geographical coverage. Thus, GPS was selected

along with compass for navigation.GPS based navigation is a

tried and tested method of navigation, with extensive past

research been carried out as in [1], which assisted us in our

work.

For collision avoidance, there were several options to

choose from in terms of selection of sensor [2].These

included infrared, LIDAR [3], camera [4] and ultrasonic

sensors. Infrared sensors [5], despite being cheap, were not a

suitable choice as in daytime outdoor operations, infrared

radiation from the Sun would have also interfered with

sensors. LIDAR is an accurate, reliable and effective

collision avoidance and obstacle detection system, however,

it is an expensive, unfeasible option compared to other

solutions and thus was not chosen. Camera for the front side

was selected for performing image processing because it is

an accurate, sophisticated method of determining obstacle

position and ensures that the vehicle moves in the correct

direction to avoid collision. For this purpose, Hard Kernel

Odroid U3, a small yet sufficiently powerful quad core

single board computer was employed to perform image

processing in minimum possible time, to enable a fast

response from the collision avoidance system. Ultrasonic

sensors have been widely used to detect and avoid obstacles

in mobile robot and automobile applications [6], [7], [8].

HC-SR04 ultrasonic sensors were chosen and installed on

the left, right and rear sides of the vehicle because of their

accuracy, speed, reliability and effectiveness in detecting

distance from obstacles in addition to the fact that more

cameras for these sides would have resulted in slow,

ineffective and resource-intensive image processing on an

overburdened SBC.

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©http://ijcer.org e- ISSN: 2278-5795 p- ISSN: 2320-9348 Page 196

Figure 1: Six ultrasonic sensors installed on vehicle; two

sensors each on the rear, left and right sides. The shaded oval

indicates position of the mounted camera.

II. OPERATING PRINCIPLES

Operating principles of major components of the system

are described below.

A. GPS

The GPS receiver calculates its position by precisely

timing the signals sent by GPS satellites. Each satellite

continually transmits messages that include the time the

message was transmitted, and the satellite position at time of

transmission.

The receiver uses the messages it receives to determine

the transit time of each message and calculates the distance

of receiver from each satellite. Each of these distances and

satellites' locations define a sphere. The receiver is on the

surface of each of these spheres when the distances and the

satellites' locations are correct. These distances and satellites'

locations are used to compute the location of the receiver

using navigation equations. The location includes the

longitude and latitude which are the GPS coordinates of the

receiver device. We utilized RMC data format of the string

which was received from GPS module.

B. Ultrasonic Sensor

A high pulse of at least 10 µs is applied to the trigger pin

of an ultrasonic sensor HC-SR04, which initiates

transmission of eight 40 kHz ultrasonic pulses. The echo pin

of the sensor goes high when the waves are transmitted.

After the waves are reflected from a surface of an object and

reach the sensor back, the echo pin goes low. The time

period of the high echo pin is proportional to the distance

measured between sensor and object [9].Distance measured

in centimeters is calculated as in (1).

(1)

III. BLOCK DIAGRAM

Figure 2: Block diagram of system.

The block diagram of the system is shown in Fig. 2.

Interrupt signal of SBC has higher priority than interrupt

signal of ATmega16 (ultrasonic system).

IV. HARDWARE

Proteus software was used for the purpose of electronic

design automation.

The following major components were used:

A. HC-SR04

HC-SR04 ultrasonic ranging modules are used to calculate

distance of obstacles from the vehicle. These sensors have a

maximum range of 500 cm and maximum effectual angle of

15⁰ [9]. Each sensor is connected to AVR and power supply

via a 4 pin wire connector.

B. ATmega16

The Atmel ATmega16 is an 8 bit microcontroller which

contains 16 KB of flash memory. It is available in 40 pin

DIP and has modified Harvard RISC architecture. It contains

32 programmable input/output lines with USART and SPI

ports [10]. Atmega16 was selected due to its low cost, easy

availability, reliability and compatibility with C language

programs.

C. ATmega162

The Atmel ATmega162 is also an 8 bit microcontroller

which contains 16 KB of flash memory. It is available in 40

pin DIP and has modified Harvard RISC architecture. It

contains 35 programmable input/output lines with USART

and SPI ports [11]. Atmega162 was selected because it has

two USART ports that were required for RFand GPS

modules.

D. Power Supplies

Separate 12 V 10 Ah rechargeable Li-Po batteries are used

to supply power to the servo and traction motors. The servo

motor was connected to the battery by means of a 12V to

7.2V voltage regulator circuitry. For the electronic circuits,

12 V 5 Ah lead-acid rechargeable battery is used which is

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stepped down to 5 V via 7805 voltage regulator IC.

E. SKM53 GPS module

The SkyNav SKM53 Series [12] with embedded GPS

antenna was used for receiving data from GPS satellites. It

was interfaced serially with ATmega162. The current GPS

coordinates of the module are calculated and sent to the

AVR from this module.

F. HMC6352 compass module

The Honeywell HMC6352 is a fully integrated compass

module that combines 2-axis magneto-resistive sensors with

the required analog and digital support circuits, and

algorithms for heading computation [13]. It was interfaced

via I2C port with ATmega162.To shield the compass module

from electromagnetic interference originating from motor, it

was kept in a grounded metal enclosure. The compass

calculates the current heading of the vehicle.

G. Camera

A generic USB webcam is mounted on the front side of

the vehicle and is connected to SBC for the purpose of image

processing.

H. Hard Kernel Odroid U3 SBC

Odroid U3 is a quad core ARM based SBC with a 1.7

GHz processor and 2 GB RAM [14]. Ubuntu Linux

operating system is installed and image processing is

performed on this SBC.

I. XBee Pro XSC RF module

XBee Pro XSC is long range 900 MHz RF module [15].

Two such modules are being used, one connected to the

user’s computer for transmitting destination coordinates and

the other connected to the system onboard the vehicle.

J. LCD

16x2 LCDs are used to display information. They are

interfaced to AVR microcontrollers.

K. H Bridge

An H bridge circuit based on the L298 H Bridge IC is

used to drive the traction motor. L298 IC can supply up to 4

A current in pulse width modulation (PWM), dual mode,

which is sufficient in this application [16]. An op to-coupler

electrically isolates AVR from the motor. Small fans and

heat sinks were installed to cool H Bridge and motors.

L. Motors

A 12 V DC motor was used for traction purpose while a

7.2 V digital servo motor having 40 kg/cm torque was

utilized for heading navigation.

V. SOFTWARE IMPLEMENTATION

Code design and debugging for programming AVR

microcontrollers was performed in Code Vision AVR in C

language. Eclipse CDT C/C++ was installed on Linux based

Odroid SBC for code design, debugging and programming

of the SBC using C++ language. The g++ for ARM Linux

was utilized for code compilation (arm-linux-gnueabihf-

g++). The flowcharts in Fig. 3-6 represent the software

implementation for each subsystem.

Figure 3: Navigation system.

Figure 4: Navigation system (continued).

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Figure 5: Ultrasonic sensory system.

Figure 6: Image processing.

VI. WORKING OF NAVIGATION SYSTEM

A. Calculation of Destination Heading Angle ( )

(2)

Angle is calculated by (2).

“ulat” is latitude of destination point entered by user.

“ulong” is longitude of destination point entered by user.

“glat” is latitude of current location of vehicle.

“glong” is latitude of current location of vehicle.

“c” is current location of vehicle (center reference point).

“d” is destination point entered by user.

The destination point quadrant determines magnitude of

the destination heading angle . Angle is represented by

the curly arrows and is calculated for each case in the

following manner, as shown in Fig. 7-10.

1st Quadrant

Figure 7: 1

st quadrant

(3)

(4)

(5)

2nd

Quadrant

Figure 8: 2nd

quadrant

(6)

(7)

(8)

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3rd

Quadrant

Figure 9: 3rd

quadrant

(9)

(10)

(11)

4th

Quadrant

Figure 10: 4

th quadrant

(12)

(13)

(14)

B. Error Computation Algorithm

“C” is the current angle of the vehicle with respect to North.

“D” or is destination heading angle.

The error e(t) is calculated using (15),(16) and (17), as

follows:

Figure 11: Difference between and C is from 0 to less

than 180 .

Therefore,

(15)

Figure 12: ( is greater than or equal to 180 .

Therefore,

(16)

Figure 13: ( is greater than or equal to 180 .

Therefore,

(17)

C. PID Control System

Figure 14: PID Control System.

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(18)

PID control system is shown in Fig. 14. The control

system adjusts the angle of servo motor and thus controls the

heading direction of the vehicle during GPS based

navigation to steer it towards destination point. The output of

the control system is given by (18) which is the sum of

proportional (P), integral (I) and derivative (D) terms

respectively.

VII. WORKING OF ULTRASONIC SYSTEM

ATmega16 polls each ultrasonic sensor and calculates

distance values. If any distance from obstacle is less than the

predefined safe threshold value (x), then the corresponding

output pin representing that sensor is set low and interrupt is

sent to ATmega162. If distance is greater than threshold,

then corresponding output is set high.

If ATmega162 is alerted by an interrupt signal, it stops

GPS based navigation to destination point and identifies the

sensor which detected obstacle and subsequently the obstacle

position, based on data received from ATmega16. Therefore,

the path of motion of vehicle is altered correspondingly by

changing servo motor angle and reducing speed of traction

motor, and thus collision is avoided. LCD 2 displays

calculated distance values.

VIII. WORKING OF IMAGE PROCESSING SYSTEM

Image processing is performed using OpenCV library by

the SBC in several steps, as shown in Fig. 15-18 [17], [18].

Obstacle is detected by edge detection method using canny

edge algorithm [19].

Figure 15: Capture image and store in matrix of size

640x480. The obstacle is the box in front of the vehicle.

Figure 16: Blur image using “opening” morphological

transformation to remove small bright objects on dark

background, which can be ignored.

Figure 17: Image converted to gray scale. Edge detection

performed using canny edge algorithm.

Figure 18: Filling initiates from left column, starting from

bottom to top. Circle indicates location of obstacle. The

image is divided into 12 rectangular portions and the

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horizontal line is 100 cm from obstacle which is the

maximum range up to which obstacles can be detected.

Number of black pixels in each portion are counted and if

they exceed the pre-defined value of 200, obstacle is

considered to be located in the associated portion(s). In such

a scenario, interrupt is sent by SBC to ATmega162. SBC

also determines direction in which vehicle is to move to

avoid collision with the detected obstacle. SBC sends data

via I/O pins to ATmega162 signaling it to head in the

appropriate direction. Therefore, ATmega162 stops GPS

based navigation, reduces vehicle speed and alters path of

motion when alerted, to avoid collision. It is important to

note that ATmega162 makes decision on altered direction of

motion based on data from both ultrasonic system and SBC.

E.g. when SBC signals ATmega162 to move in reverse

direction, the AVR also checks if obstacle is near the rear

side via data from the ultrasonic system.

IX. RESULTS

After conducting extensive tests on flat terrain, it was

observed that the vehicle navigates and accurately reaches

user-defined destination coordinates via shortest straight

path, using GPS and compass modules. Furthermore, any

nearby stationary or moving obstacles within 100cm of the

front side were detected and collision avoided with

negligible reaction time, with the help of image processing,

when the maximum speed of vehicle was 40 km/h. In the

case of ultrasonic system, the maximum range of obstacle

detection was up to 500cm, thus ensuring collision

avoidance with detected stationary and moving obstacles at

vehicle speeds in excess of 60 km/h. When the threshold

distance (x) was set as70cm, the vehicle avoided rear and

sideways collisions even at an approximate speed of 35

km/h.

X. CONCLUSION AND FUTURE SCOPE

The developed system is useful as it provides autonomous

driving ability to vehicles while at the same time avoids

collision.

However, there are a few limitations associated with this

system. Shadows and darkness can be considered as

obstacles by the image processing system due to the

boundary between dark and light color portions of image

being considered as an edge. This problematic phenomenon

can be prevented from occurring by incorporating wide beam

headlights on vehicle or navigating vehicle in well-lit areas.

Usage of vehicle on bright sunny days is also suitable.

Rough terrain or very small objects on the ground can also

be considered as obstacles therefore the vehicle should be

driven on mostly flat terrain. Another solution is to increase

blurring of image at the cost of additional processing power

required and subsequent delay in obstacle detection, in order

to ignore very small obstacles and edges on rough terrain.

Obstacles that have surface of sound absorbing material

reflect a very small amount of ultrasonic waves. This

problem can be solved by using LIDAR with the

disadvantage of high financial expenditure. Another

disadvantage is that irregular surfaces do not reflect waves

towards the sensor because the incident wave is not

perpendicular to obstacle surface causing reflection away

from the sensor [20] [21] [22].

To improve the effectiveness of the collision avoidance

system, the safe threshold value of distance for the ultrasonic

system can be increased up to 500cm. Furthermore, using a

higher resolution camera can detect very small obstacles

more easily. Using a wide angle camera can increase the

visual field of the image processing system and thus increase

its effectiveness in detecting and avoiding obstacles which

are present in front of the vehicle.

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gps based navigation and collision avoidance system using ultrasonic sensors and image processing...

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