balancing robot atmega328

8/18/2019 Balancing Robot ATMEGA328

1/52

1

TWO WHEEL BALANCING ROBOT USING

MICROCONTROLLER ATMEGA 328P

A DISSERTATION

Submitted to

Faculty of Engineering and Technology

For the award of degree of

Bachelor of Technology

(Electronics and Communication Engineering)

Supervisor:

Er. INDERPREET SINGH Submitted by:

MITUL TAKIAR

(2011ECA1760)

PRANAV SHARMA

(2011ECA1069)

Department of Electronics TechnologyGuru Nanak Dev University

Amritsar – 143005

India


2/52

2

DECLARATION

We hereby declare that the project work entitled as “TWO WHEEL BALANCING

ROBOT USING MICROCONTROLLER ATMEGA 328P” is an authentic record of our own

work carried out at Guru Nanak Dev University, Amritsar as required for the six months project

semester for the award of degree of B.Tech (Electronics and Communication Engineering), under

the guidance of Er. Inderpreet Singh, during Jan 2014 to April 2014.

Date:


3/52

3

ACKNOWLEDGEMENT

Acknowledgement is not a mere formality but a genuine attempt to remember all those people without

whose cooperation we would not have been able to complete our project.

We want to thank the Department of Electronics Technology, Guru Nanak Dev University, Amritsar

for giving us such a golden opportunity to commence this project in the first instance. We express our

sincere gratitude to Dr. Maninder Lal Singh, Head of the department, Electronics Technology who

helped us turn this opportunity into true results.

We extend our thanks to ”Er. Inderpreet Singh” who encouraged us to go ahead with our project.

Without his able guidance and counsel it would have been impossible for us to complete this project.

We would like to thank GOD, the Almighty, for having made everything possible by giving us strength

and courage to do this work. Lastly, we wish to express our sincere appreciation to our parents for their

patience and encouragement during this work .


4/52

4

ABSTRACT

The project is designed to build a two wheel balancing robotic vehicle using

MPU6050 for its movement. A microcontroller of AVR family is used to achieve the

desired operation.

A robot is a machine that can perform task automatically or with guidance. Robotics

is generally a combination of computational intelligence and physical machines

(motors). Computational intelligence involves the programmed instructions.

The balancing robot platform proved to be an excellent test bed for sensor fusion

using the Kalman filter . An indirect Kalman filter configuration combining a piezo

rate gyroscope sensor and an accelerometer is implemented to obtain an accurate

estimate of the tilt angle and its derivative.

Depending on the input signal received, the microcontroller redirects the robot to

move in an alternate direction by actuating the motors interfaced to it through a motor

driver IC.


5/52

5

TABLE OF CONTENTS

Declaration...................................................................................................................... 02

Acknowledgement............................................................................................................. 03

Abstract ............................................................................................................................ 04

List of figures..................................................................................................................... 07

List of tables ........................................................... ........................................................... 07

1. Introduction ..............................................................................................................08

1.1. Project Description .............................................................................................08

1.2. Applications ........................................................................................................08

2. Literature Review .....................................................................................................09

2.1. Complementary Filter..........................................................................................09

2.2. Kalman Filter............................................................... ........................................11

2.3. PID Controller.............................................................................................. .......14

2.3.1. PID controller theory..............................................................................15

2.3.2. Limitations of PID controller.................................................................17

3. Components Review .................................................................................................19

3.1. ATmega328.........................................................................................................19

3.1.1. Block Diagram........................................................................................20

3.1.2. Pin Diagram............................................................................................21

3.1.3. Features...................................................................................................23

3.2. MPU6050.............................................................................................................24

3.3. Bidirectional level converter................................................................................27

3.3.1. Circuit.....................................................................................................27

3.3.2. Features...................................................................................................27

3.4. L293D......................................................................................................... .........29

3.4.1. Block Diagram........................................................................................29

3.4.2. Pin Diagram............................................................................................30

3.4.3. Features...................................................................................................31

3.4.4. Circuit Diagram......................................................................................31

3.5. LM7805................................................................................................. ...............32

3.5.1. Pin Description........................................................................................32

3.5.2. Circuit......................................................................................................32

3.6. LM317................................................................................................... ...............33

3.6.1. Features....................................................................................................33


6/52

6

3.6.2. Circuit......................................................................................................34

4. Assembling the Robot ................................................................................................35

5. Programming the robot................................................................................................39

6. Testing the robot..........................................................................................................44

6.1. PID Control Unit...................................................................................................44

6.2. Motor Speed Control.............................................................................................45

6.3. Sensor Check.............................................................................................. ..........47

Bibliography ............................................................................................................................52


7/52

7

LIST OF FIGURES

Figure no. Caption Page no.

1. Principle of complementary filter........................................................................................09

2. Kalman Filter in inertial navigation.....................................................................................12

3. PID Controller block diagram.............................................................................................14

4. Plot of PV vs time for three values of K p (Ki and Kd held constant)………………….….16

5. Plot of PV vs time for three values of Ki ( Kp and Kd held constant)…………………....16

6. Plot of PV vs time for three values of Kd ( Ki and Kp held constant)…………………....17

7. ATmega328.........................................................................................................................19

8. ATmega328 block diagram.................................................................................................20

9. ATmega328 pin diagram.....................................................................................................21

10. MPU6050............................................................................................................................2411. Bidirectional level converter...............................................................................................27

12. I2C using MOSFET.............................................................................................................28

13. L293D block diagram..........................................................................................................29

14. L293D pin diagram..............................................................................................................30

15. L293D Circuit diagram.........................................................................................................31

16. LM7805................................................................................................................................32

17. LM7805 Connection diagram...............................................................................................32

18. LM317...................................................................................................................................33

19. LM317 Circuit Diagram.......................................................................................................34

20. Creating a new AVR Studio-4 project..................................................................................39

21. Creating a new AVR Studio-4 project..................................................................................40

22. Building a project with AVR Studio.....................................................................................40

23. Connecting to the programmer with AVR Studio............... ...................... ........................ ....41

24. AVR Studio-4’s programmer selection dialog box..................... .................... ...................... .41

25. Selecting the device for ISP programming.................... ........................ ..................... ...........42

26. Reading the device signature.................................................................................................42

27. AVR Studio’s program ISP tab.............................................................................................43

LIST OF TABLES

Table no. Caption Page no.

1. Comparison among ATmega variants.............................................................................19

2. Pin description of L293D................................................................................................30

3. Pin description of LM7805..............................................................................................32


8/52

8

CHAPTER 1

INTRODUCTION

1.1 Project Description

The research on balancing robot has gained momentum over the last decade in a number of

robotics laboratories around the world. This is due to the inherent unstable dynamics of the

system. Such robots are characterised by the ability to balance on its two wheels and spin on

the spot. This additional manoeuvrability allows easy navigation on various terrains, turn

sharp corners and traverse small steps or curbs. These capabilities have the potential to solve a

number of challenges in industry and society.

A balancing robot is built as a platform to investigate the use of a Kalman filter for sensorfusion. The Kalman filter approach to sensor fusion is unprecedented. This would be a new

avenue to explore the filter for future potential applications of the Kalman filter.

Apart from the above, this thesis will delve into the suitability and performance of linear state

space controllers namely the Linear Quadratic Regulator (LQR) and a Pole placement

controller in balancing the system. The robot utilises a Proportional-Integral- Derivative (PID)

controlled differential steering method for trajectory control. A gyroscope and inclinometer is

used to measure the tilt of the robot and the encoders on the motors to measure the wheel’s

rotation.

1.2 Applications

A motorised wheelchair utilising this technology would give the operator great

manoeuvrability and thus access to places most able-bodied people take for granted.

Climbing up the staircase can be accomplished by using this robot to balance on two

wheels.

Small carts built utilising this technology allows humans to travel short distances in a

small area or factories as opposed to using cars or buggies which is more polluting.


9/52

9

CHAPTER 2

LITERATURE REVIEW

2.1 Complementary Filter

Complementary filters are defined in mathematical terms and in the context of Weiner and Kalman

filters. The derivation of common forms is explored, and it is shown why a Kalman filter is often used

within a complementary filter structure. An example of the design of a complementary filter for a

practical application is presented in detail.

Introduction

The term “complementary filter” is often casually used in the literature to refer to any digital algorithm

that serves to “blend” or “fuse” similar or redundant data from different sensors to achieve a robust

estimate of a single state variable. For example, in aerospace navigation systems, a complementary

filter is often utilized to estimate the position in space of an airframe by combining the high resolution

position information obtained from integrating acceleration and velocity data with the low resolution

position information obtained directly from the GPS satellite network. The data available from an

inertial navigation systems is very good information for a short period of time. However, as integration

errors grow in an unbounded fashion, they can no longer be tolerated. On the other hand, the position

errors associated with GPS data, though quite large, are bounded and well characterized. A

complementary filter combines the excellent high frequency position information derived from the

integration of inertial sensor data with the good lowfrequency position information from GPS data, while rejecting the errors peculiar to each method. The

reader should note that complementary filters are in a class by themselves. While filters in general act

on a signal, the complementary filter does not. It acts only on the different kinds of noise associated

with different kinds of measurements of the same signal. It is a solution waiting for a very special

problem - that of estimating a state variable from data from multiple sources, which exhibit noise with

different frequency content.

Mathematical definition

The complementary filter is a frequency domain filter. In its strictest sense, the definition of a

complementary filter refers to the use of two or more transfer functions, which are mathematical

complements of one another. Thus, if the data from one sensor is operated on by G(s), then the data

from the other sensor is operated on by I-G(s), and the sum of

the transfer functions is I, the identity matrix. In the case of a one-dimensional filter as will be

described in this paper, the identity matrix reduces to the scalar number one.

In a typical two -input system, one input will provide information with high frequency noise, and is

thus low-pass filtered. The other input provides information with low frequency noise, and is high-pass

filtered. If the low-pass and high-pass filters are mathematical complements, then the output of the


10/52

10

filter is the complete reconstruction of the variable being sensed, minus the noise associated with the

sensors.

A block diagram illustrating this process with “perfect” 1st-order low-pass and high-pass filters is

shown below:

Figure 1 Principle of complementary filter

A simple estimation technique that is often used in the flight control industry to combine measurements

is the complementary filter . This filter is actually a steady state Kalman filter (i.e., a Wiener filter) for

a certain class of filtering problems. This relationship does not appear to be well known by many

practitioners of either complementary or Kalman filtering. One exception is the tutorial paper by

Brown which discusses this relationship without going into the mathematical details. The

complementary filter users do not consider any statistical description for the noise corrupting the

signals, and their filter is obtained by a simple analysis in the frequency domain. The proponents of the

Kalman filtering approach work in the time domain and do not pay much attention to the transfer

function or frequency domain (Wiener filter) approach to the filtering problem, since it is a less general

approach to the filtering problem. The Wiener filter solution to this class of multiple-input estimation

problems appeared in the literature, well before Kalman published his classic paper. This paper reviews

complementary filtering and shows how this technique is related to Kalman and Wiener filtering. Since

both Kalman and complementary filtering are under consideration for use in the Space Shuttle Reentry

and Landing Navigation System, the relationship between them should be well understood.


11/52

11

2.2 Kalman Filter

Kalman filters, as they are used in navigation systems, are based on the complementary filtering

principle. The basic block diagram is given in Fig. 5, although, as in the previous cases, the actual

implementation may be different. Note the similarity between Fig.5 and Fig.1(B). The complementary

constraint means that the filter just operates on the noise and is not affected by actual signals that are to

be estimated. The advantages and disadvantages of removing this constraint are discussed as follows:

In applying Kalman filtering to the problem of combining noisy measurements, the philosophy used is

that the processing of one class of measurements defines the basic process equations. The other

measurements, sometimes referred to as augmenting measurements, define the measurement equations

for the filter. After discussing the basic equations, the two examples of the previous section are

reworked using the steady-state Kalman filter approach. These examples can also be solved by the

Wiener filter approach using spectrum factorization. The relationship between the steady-state or

stationary Kalman filter and the Wiener filter is discussed in the book by Sage and Melsa [6].

Basically, there are two measurements, one of which serves as an input to a differential equation which

serves as the process model. The ideal equations are

xI = FxI + gu (process)

zI = hxI (measurement)

where u is one noiseless measurement and zI is the other. F, g, h, and x are n*n, n*1, 1*n, and n*1

matrixes, respectively; zj and u are scalars. In actuality, we have two noisy measurements, so that the

equations are

x = Fx + g(u + w)

z =hxI + v

where w and v are zero-mean, white, Gaussian noise.

The error equations are


12/52

12

where 6x is the error vector.

The Kalman filter equation is

Figure 2 Typical application of the Kalman filter in inertial navigation

where 6k is the estimate of the error vector and k is the Kalman filter gain. k, an n*1 matrix, is obtained

from the equations

where P, the n*n error covariance matrix, is the solution of the Riccati equation

in which R = u2 is the variance of the measurement noise and Q = u2 is the variance of the process

noise. The stationary Kalman filter is obtained by setting P = 0 in the Riccati equation. The actual

estimates of the signals are

In order to show the relationship with the complementary filters, the above equations can be

manipulated to produce a differential equation for directly:

As is shown below, this equation is identical to the differential equations of the complementary filters

for the example under consideration.


13/52

13

Example 1

The process equation from Fig. 2(A) is

Therefore, F = 0, g = 1, and h = 1, so that the algebraic Riccati equation is

The filter equation is obtained by substituting into above:

This equation is identical to the equation of the complementary filter in Fig. 2(B), where the time

constant of the filter is now Note that a time constant of four, as in the complementary

filter, means that the barometric signal is assumed to be much noisier than the accelerometer signal. In

the complementary filter, the time constant is chosen to get most of the information from the

accelerometer signal and use the barometric information only as along-term reference.


14/52

2.3 PID Controller

A proportional-integral-derivative

mechanism (controller) widely

Controllers, SCADA systems, Re

as the difference between a meas

to minimize the error in outputs by

The PID controller algorithm in

sometimes called three-term

denoted P, I, and D. Simply put,

the present error, I on the accumul

current rate of change. The weig

control element such as the positi

element.

In the absence of knowledge o

considered to be the best controlle

controller can provide control acti

controller can be described in ter

which the controller overshoots th

the PID algorithm for control does

Some applications may require usi

This is achieved by setting the oth

controller in the absence of the

derivative action is sensitive to

prevent the system from reaching i

14

controller (PID controller) is a control lo

used in industrial control systems (Programma

ote Terminal Units etc). A PID controller calculates an "

red process variable and a desired set point. The control

adjusting the process control inputs.

olves three separate constant parameters, and is

control: the proportional, the integral and deriv

hese values can be interpreted in terms of time: P

ation of past errors, and D is a prediction of future error

ted sum of these three actions is used to adjust the p

on of a control valve, a damper, or the power supplied

the underlying process, a PID controller has histor

. By tuning the three parameters in the PID controller al

on designed for specific process requirements. The resp

s of the responsiveness of the controller to an error, t

set point, and the degree of system oscillation. Note th

not guarantee optimal control of the system or system st

igure 3 PID controller block diagram

g only one or two actions to provide the appropriate sys

r parameters to zero. A PID controller will be called a P

espective control actions. PI controllers are fairly co

easurement noise, whereas the absence of an integra

ts target value due to the control action.

p feedback

le Logic

error" value

ler attempts

accordingly

tive values,

depends on

s, based on

ocess via a

o a heating

ically been

orithm, the

onse of the

e degree to

t the use of

bility.

em control.

, PD, P or I

mon, since

l term may


15/52

2.3.1 PID controller theory

The PID control scheme is na

manipulated variable (MV). The

the output of the PID controller.

algorithm is:

where

: Proportional gain, a

: Integral gain, a tuni

: Derivative gain, a t

: Error

: Time or instantaneous

: Variable of integratio

SP: Set point

PV: Process Variable

Proportional Term

The proportional term produces a

proportional response can be adjus

gain constant.

The proportional term is given by:

A high proportional gain results i

proportional gain is too high, the

small output response to a large i

proportional gain is too low, th

disturbances. Tuning theory and in

the bulk of the output change.

15

ed after its three correcting terms, whose sum con

roportional, integral, and derivative terms are summed

Defining as the controller output, the final form

tuning parameter

g parameter

uning parameter

time (the present)

; takes on values from time 0 to the present .

n output value that is proportional to the current error

ted by multiplying the error by a constant K p, called the

a large change in the output for a given change in the

system can become unstable. In contrast, a small gain

nput error, and a less responsive or less sensitive contr

e control action may be too small when responding

dustrial practice indicate that the proportional term shoul

stitutes the

to calculate

of the PID

value. The

roportional

error. If the

results in a

oller. If the

to system

d contribute


16/52

Figure 4Plot of PV

Integral Term

The contribution from the integr

duration of the error. The integral

and gives the accumulated offset t

then multiplied by the integral gai

The integral term is given by:

The integral term accelerates the

steady-state error that occurs wit

responds to accumulated errors fr

value

Figure 5Plot of PV

16

vs time, for three values of Kp (Ki and Kdheld constant)

l term is proportional to both the magnitude of the er

in a PID controller is the sum of the instantaneous erro

at should have been corrected previously. The accumul

( ) and added to the controller output.

ovement of the process towards set point and eliminates

a pure proportional controller. However, since the i

m the past, it can cause the present value to overshoot t

vs time, for three values of Ki (Kp and Kdheld constant)

ror and the

r over time

ted error is

the residual

tegral term

he set point


17/52

Derivative Term

The derivative of the process erro

multiplying this rate of change b

derivative term to the overall contr

The derivative term is given by:

Derivative action predicts syste

system. An ideal derivative is n

additional low pass filtering f

noise. Derivative action is seldom

controllers - because of its variable

Figure 6Plot of PV

2.3.2 Limitations of PID co

While PID controllers are appli

without any improvements or only

not in general provide optimal co

feedback system, with constant pa

performance is reactive and a co

without a model of the process, b

the process without resorting to an

PID controllers, when used alone,

so that the control system does no

also have difficulties in the presen

17

is calculated by determining the slope of the error ov

the derivative gain K d . The magnitude of the contrib

ol action is termed the derivative gain, K d .

behaviour and thus improves settling time and stab

t causal, so that implementations of PID controllers

r the derivative term, to limit the high frequenc

used in practice though - by one estimate in only 20%

impact on system stability in real-world applications.

vs time, for three values of Kd (Kp and Kiheld constant)

troller

able to many control problems, and often perform s

coarse tuning, they can perform poorly in some applicati

ntrol. The fundamental difficulty with PID control is

ameters, and no direct knowledge of the process, and

promise. While PID control is the best controller in

tter performance can be obtained by overtly modelling

observer.

can give poor performance when the PID loop gains mus

t overshoot, oscillate or hunt about the control set point

ce of non-linearities, may trade-off regulation versus res

er time and

tion of the

ility of the

include an

gain and

of deployed

atisfactorily

ons, and do

that it is a

hus overall

n observer

the actor of

be reduced

value. They

ponse time,


18/52

18

do not react to changing process behaviour (say, the process changes after it has warmed up), and have

lag in responding to large disturbances.

The most significant improvement is to incorporate feed-forward control with knowledge about the

system, and using the PID only to control error. Alternatively, PIDs can be modified in more minor

ways, such as by changing the parameters (either gain scheduling in different use cases or adaptively

modifying them based on performance), improving measurement (higher sampling rate, precision, and

accuracy, and low-pass filtering if necessary), or cascading multiple PID controllers.

Linearity

Another problem faced with PID controllers is that they are linear, and in particular symmetric. Thus,

performance of PID controllers in non-linear systems (such as HVAC systems) is variable. For

example, in temperature control, a common use case is active heating (via a heating element) but

passive cooling (heating off, but no cooling), so overshoot can only be corrected slowly – it cannot be

forced downward. In this case the PID should be tuned to be over damped, to prevent or reduce

overshoot, though this reduces performance (it increases settling time).

Noise in derivative

A problem with the derivative term is that it amplifies higher frequency measurement or

process noise that can cause large amounts of change in the output. It does this so much, that a physical

controller cannot have a true derivative term, but only an approximation with limited bandwidth. It is

often helpful to filter the measurements with a low-pass filter in order to remove higher-frequency

noise components. As low-pass filtering and derivative control can cancel each other out, the amount of

filtering is limited. So low noise instrumentation can be important. A nonlinear median filter may be

used, which improves the filtering efficiency and practical performance. In some cases, the differential

band can be turned off with little loss of control. This is equivalent to using the PID controller as a PI

controller.


19/52

19

CHAPTER 3

COMPONENTS REVIEW

3.1 ATmega 328

A highly integrated chip that contains all the components comprises a controller. Typically this

includes a CPU, RAM, some form of ROM, I/O ports, and timers. Unlike a general-purpose computer,

which also includes all of these components, a microcontroller is designed for a very specific task; to

control a particular system. As a result, the parts can be simplified and reduced, which cuts down on

production costs.

Figure 7ATmega 328

Microcontrollers are sometimes called embedded microcontrollers. This just means that they are part of

an embedded system; that is, one part of a larger device or system. Microcontrollers are used in

automatically controlled products and devices, such as automobile engine control systems, implantable

medical devices, remote controls, office machines, appliances, power tools, toys and other embedded

systems. The first integrated circuit was developed by Jack Kilby of Texas Instruments and Robert

Noyce of Fairchild Semiconductor in 1950.

Comparison between ATmega48PA, ATmega88PA,ATmega168PA and ATmega328P

The ATmega48PA, ATmega88PA, ATmega168PA and ATmega328P differ only in memory sizes,

boot loader support, and interrupt vector sizes. Table summarizes the different memory and interrupt

vector sizes for the three devices.

Device Flash EEPROM RAM Interrupt Vector Size

ATmega48PA 4K Bytes 256 Bytes 512 Bytes 1 instruction word/vector

ATmega88PA 8K Bytes 512 Bytes 1K Bytes 1 instruction word/vector

ATmega168PA 16K Bytes 512 Bytes 1K Bytes 2 instruction words/vector

ATmega328P 32K Bytes 1K Bytes 2K Bytes 2 instruction words/vector

Table 1


20/52

20

3.1.1 Block Diagram:

The AVR core combines a rich instruction set with 32 general purpose working registers. All the 32

registers are directly connected to the Arithmetic Logic Unit (ALU), allowing two independent

registers to be accessed in one single instruction executed in one clock cycle. The resulting architecture

is more code efficient while achieving throughputs up to ten times faster than conventional CISC

microcontrollers.

Figure 8 ATmega328 block diagram


21/52

21

3.1.2 Pin Diagram:

Figure 9 ATmega328 pin diagram

VCC

Digital supply voltage.

GND

Ground.

Port B (PB7:0) XTAL1/XTAL2/TOSC1/TOSC2

Port B is an 8-bit bi-directional I/O port with internal pull-up resistors (selected for each bit). The Port

B output buffers have symmetrical drive characteristics with both high sink and source capability. As

inputs, Port B pins that are externally pulled low will source current if the pull-up resistors are

activated. The Port B pins are tri-stated when a reset condition becomes active, even if the clock is not

running. Depending on the clock selection fuse settings, PB6 can be used as input to the inverting

Oscillator amplifier and input to the internal clock operating circuit. Depending on the clock selection

fuse settings, PB7 can be used as output from the inverting Oscillator amplifier. If the Internal

Calibrated RC Oscillator is used as chip clock source, PB7..6 is used as TOSC2..1input for the

Asynchronous Timer/Counter2 if the AS2 bit in ASSR is set.

Port C (PC5:0)

Port C is a 7-bit bi-directional I/O port with internal pull-up resistors (selected for each bit). The PC5..0

output buffers have symmetrical drive characteristics with both high sink and source capability. As

inputs, Port C pins that are externally pulled low will source current if the pull-up resistors are


22/52

22

activated. The Port C pins are tri-stated when a reset condition becomes active, even if the clock is not

running.

PC6/RESET

If the RSTDISBL Fuse is programmed, PC6 is used as an I/O pin. Note that the electrical

characteristics of PC6 differ from those of the other pins of Port C. If the RSTDISBL Fuse isunprogrammed, PC6 is used as a Reset input. A low level on this pin for longer than the minimum

pulse length will generate a Reset, even if the clock is not running.

Port D (PD7:0)

Port D is an 8-bit bi-directional I/O port with internal pull-up resistors (selected for each bit). The Port

D output buffers have symmetrical drive characteristics with both high sink and source capability. As

inputs, Port D pins that are externally pulled low will source current if the pull-up resistors are

activated. The Port D pins are tri-stated when a reset condition becomes active, even if the clock is not

running.

AVCC

AVCC is the supply voltage pin for the A/D Converter, PC3:0, and ADC7:6. It should be externally

connected to VCC, even if the ADC is not used. If the ADC is used, it should be connected to VCC

through a low-pass filter. Note that PC6..4 use digital supply voltage, VCC.

AREF

AREF is the analog reference pin for the A/D Converter.


23/52

23

3.1.3 FEATURES

Manufacturer: Atmel

Product Category: 8-bit Microcontrollers - MCU

Core: AVR

Data Bus Width: 8 bit

Maximum Clock Frequency: 20 MHz

Program Memory Size: 32 kB

Data RAM Size: 2 kB

On-Chip ADC: Yes

Operating Supply Voltage: 1.8 V to 5.5 V

Maximum Operating Temperature: + 85 C

Package / Case: PDIP-28

Mounting Style: Through Hole

A/D Bi t Si ze: 10 bit

A/D Channels Availab le: 8

Brand: Atmel

Data Ram Type: SRAM

Data ROM Size: 1 kB

Data Rom Type: EEPROM

Interface Type: I2C, SPI, USART

Minimum Operating Temperature: - 4 0 C

Number of Progr ammable I/Os: 23

Number of Timers: 3

Packaging: Tube

Processor Series: megaAVR

Product Category: Microcontrollers - AVR

Program Memory Type: Flash

Series: ATMEGA328

Supply Voltage - Max: 5.5 V

Supply Voltage - Min: 1.8 V


24/52

24

3.2 MPU6050

Figure 10 MPU 6050

Motion Interface is becoming a “must-have” function being adopted by smart phone and tablet

manufacturers due to the enormous value it adds to the end user experience. In smartphones , it finds

use in applications such as gesture commands for applications and phone control, enhanced gaming,

augmented reality, panoramic photo capture and viewing, and pedestrian and vehicle navigation. With

its ability to precisely and accurately track user motions, Motion Tracking technology can convert

handsets and tablets into powerful 3D intelligent devices that can be used in applications ranging from

health and fitness monitoring to location-based services. Key requirements for Motion Interface

enabled devices are small package size, low power consumption, high accuracy and repeatability, high

shock tolerance, and application specific performance programmability – all at a low consumer price

point.

The MPU-60X0 is the world’s first integrated 6-axis Motion Tracking device that combines a 3-axis

gyroscope, 3-axis accelerometer, and a Digital Motion Processor (DMP) all in a small 4x4x0.9mm

package. With its dedicated I2C sensor bus, it directly accepts inputs from an external 3-axis compass

to provide a complete 9-axis Motion Fusion output. The MPU-60X0 Motion Tracking device, with its

6-axis integration, on-board Motion Fusion, and run-time calibration firmware, enables manufacturers

to eliminate the costly and complex selection, qualification, and system level integration of discrete

devices, guaranteeing optimal motion performance for consumers. The MPU-60X0 is also designed to

interface with multiple non-inertial digital sensors, such as pressure sensors, on its auxiliary I2C port.

The MPU-60X0 is footprint compatible with the MPU-30X0 family.

The MPU-60X0 features three 16-bit analog-to-digital converters (ADCs) for digitizing the gyroscope

outputs and three 16-bit ADCs for digitizing the accelerometer outputs. For precision tracking of both

fast and slow motions, the parts feature a user-programmable gyroscope full-scale range of ±250, ±500,

±1000, and ±2000°/sec (dps) and a user-programmable accelerometer full-scale range of ±2g, ±4g,

±8g, and ±16g.


25/52

25

An on-chip 1024 Byte FIFO buffer helps lower system power consumption by allowing the system

processor to read the sensor data in bursts and then enter a low-power mode as the MPU collects more

data. With all the necessary on-chip processing and sensor components required to support many

motion-based use cases, the MPU-60X0 uniquely enables low-power Motion Interface applications in

portable applications with reduced processing requirements for the system processor. By providing an

integrated Motion Fusion output, the DMP in the MPU-60X0 offloads the intensive Motion Processing

computation requirements from the system processor, minimizing the need for frequent polling of the

motion sensor output.

Communication with all registers of the device is performed using either I2C at 400kHz or SPI at

1MHz (MPU-6000 only). For applications requiring faster communications, the sensor and interrupt

registers may be read using SPI at 20MHz (MPU-6000 only). Additional features include an embedded

temperature sensor and an on-chip oscillator with ±1% variation over the operating temperature range.

By leveraging its patented and volume-proven Nasiri-Fabrication platform, which integrates MEMS

wafers with companion CMOS electronics through wafer-level bonding, InvenSense has driven the

MPU-60X0 package size down to a revolutionary footprint of 4x4x0.9mm (QFN), while providing the

highest performance, lowest noise, and the lowest cost semiconductor packaging required for handheld

consumer electronic devices. The part features a robust 10,000g shock tolerance, and has

programmable low-pass filters for the gyroscopes, accelerometers, and the on-chip temperature sensor.

For power supply flexibility, the MPU-60X0 operates from VDD power supply voltage range of

2.375V-3.46V. Additionally, the MPU-6050 provides a VLOGIC reference pin (in addition to its

analog supply pin: VDD), which sets the logic levels of its I2C interface. The VLOGIC voltage may be

1.8V±5% or VDD.

The MPU-6000 and MPU-6050 are identical, except that the MPU-6050 supports the I 2C serial

interface only, and has a separate VLOGIC reference pin. The MPU-6000 supports both I2C and SPI

interfaces and has a single supply pin, VDD, which is both the device’s logic reference supply and the

analog supply for the part.

Features

Gyroscope FeaturesThe triple-axis MEMS gyroscope in the MPU-60X0 includes a wide range of features:

Digital-output X-, Y-, and Z-Axis angular rate sensors (gyroscopes) with a user- programmable full-scale range of ±250, ±500, ±1000, and ±2000°/sec

External sync signal connected to the FSYNC pin supports image, video and GPS

synchronization

Integrated 16-bit ADCs enable simultaneous sampling of gyros

Enhanced bias and sensitivity temperature stability reduces the need for user calibration

Improved low-frequency noise performance


26/52

26

Digitally-programmable low-pass filter

Gyroscope operating current: 3.6mA

Standby current: 5µA

Factory calibrated sensitivity scale factor

User self-test

Accelerometer Features

The triple-axis MEMS accelerometer in MPU-60X0 includes a wide range of features:

Digital-output triple-axis accelerometer with a programmable full scale range of ±2g, ±4g,

±8g and ±16g

Integrated 16-bit ADCs enable simultaneous sampling of accelerometers while requiring no

external multiplexer

Accelerometer normal operating current: 500µA

Low power accelerometer mode current: 10µA at 1.25Hz, 20µA at 5Hz, 60µA at 20Hz,

110µA at 40Hz Orientation detection and signaling

Tap detection

User-programmable interrupts

High-G interrupt

User self-test

Additional Features

The MPU-60X0 includes the following additional features:

9-Axis MotionFusion by the on-chip Digital Motion Processor (DMP)

Auxiliary master I2C bus for reading data from external sensors (e.g., magnetometer)

3.9mA operating current when all 6 motion sensing axes and the DMP are enabled

VDD supply voltage range of 2.375V-3.46V

Flexible VLOGIC reference voltage supports multiple I2C interface voltages (MPU-6050

only)

Smallest and thinnest QFN package for portable devices: 4x4x0.9mm

Minimal cross-axis sensitivity between the accelerometer and gyroscope axes

1024 byte FIFO buffer reduces power consumption by allowing host processor to read the

data in bursts and then go into a low-power mode as the MPU collects more data

Digital-output temperature sensor

User-programmable digital filters for gyroscope, accelerometer, and temp sensor

10,000 g shock tolerant

400kHz Fast Mode I2C for communicating with all registers

1MHz SPI serial interface for communicating with all registers (MPU-6000 only)


27/52

27

3.3 Bidirectional level converter

Bi-directional level converter is a small device that safely steps down either 5volt signals to 3.3volt or

steps up 3.3volt to 5volt. This level converter also works with 2.8volt and 1.8volt devices. Each level

converter has the capability of converting 4 pins on the high side to 4 pins on the low side with two

inputs and two outputs provided for each side. One input on each side is positive voltage and the other

is ground.

The level converter is very easy to use. The board needs to be powered from the two voltages sources

(high voltage and low voltage) that your system is using. High voltage (5V for example) to the 'HV'

pin, low voltage (2.8V for example) to 'LV', and ground from the system to the 'GND' pin.

This revision of the Logic Level Converter fixes the issue with the board not stepping down from 5V to

3.3V correctly.

3.3.1 Circuit:

Figure 11 Bidirectional level converter

3.3.2 Features:

Minimum Voltage: 3.3V and Maximum Voltage: 5V

Bi-directional Logic Level conversion is possible.

BreadBoard friendly.


28/52

28

Bi-Directional MOSFET Voltage Level Converter 3.3V to 5V

When connecting 3.3V devices and 5V devices voltage level conversion is required. The following

circuit will allow this to be done bi-directionally:

Figure 12 I2C using MOSFET

Low Side Control

When the low side (3.3V) device transmits a '1' (3.3V), the MOSFET is tied high (off), and the high

side sees 5V through the R2 pull-up resistor. When the low side transmits a '0' (0V), the MOSFET

source pin is grounded and the MOSFET is switched on and the high side is pulled down to 0V.

High Side Control

When the high side transmits a '0' (0V) the MOSFET substrate diode conducts pulling the lowside

down to approx 0.7V, this is also low enough to turn the MOSFET on, further pulling the low side

down. When the high side transmits a '1' (5V) the MOSFET source pin is pulled up to 3.3V and the

MOSFET is OFF.

This works with I2C.


29/52

29

3.4 L293D

The L293 and L293D are quadruple high-current half-H drivers. The L293 is designed to provide

bidirectional drive currents of up to 1 A at voltages from 4.5 V to 36 V. The L293D is designed to

provide bidirectional drive currents of up to 600-mA at voltages from 4.5 V to 36 V. Both devices are

designed to drive inductive loads such as relays, solenoids, dc and bipolar stepping motors, as well as

other high-current/high-voltage loads in positive-supply applications.

All inputs are TTL compatible. Each output is a complete totem-pole drive circuit, with a Darlington

transistor sink and a pseudo Darlington source. Drivers are enabled in pairs, with drivers 1 and 2

enabled by 1,2EN and drivers 3 and 4 enabled by 3,4EN. When an enable input is high, the associated

drivers are enabled, and their outputs are active and in phase with their inputs. When the enable input is

low, those drivers are disabled, and their outputs are off and in the high-impedance state. With the

proper data inputs, each pair of drivers forms a full-H (or bridge) reversible drive suitable for solenoid

or motor applications.

3.4.1 Block Diagram

Figure 13 L293D Block Diagram


30/52

30

3.4.2 Pin Diagram:

Figure 14 L293D Pin Diagram

Pin Description:

Table 2


31/52

31

3.4.3 Features:

Driver Case Style:DIP

Motor Type:Half-H

No. of Outputs:4

No. of Pins:16

Operating Temperature Max:70°C

Operating Temperature Min:0°C

Output Current:600mA

Output Voltage:36V

Supply Voltage Max:36V

Supply Voltage Min:4.5V

Supply Voltage Range:4.5V to 36V

3.4.4 Circuit Diagram

Figure 15 L293D Circuit Diagram


32/52

32

3.5 LM7805

A voltage regulator is a circuit that supplies a constant voltage regardless of changes in load current.

7805 is a voltage regulator integrated circuit. It is a member of 78xx series of fixed linear voltage

regulator ICs. The voltage source in a circuit may have fluctuations and would not give the fixed

voltage output. The voltage regulator IC maintains the output voltage at a constant value. The xx in

78xx indicates the fixed output voltage it is designed to provide. 7805 provides +5V regulated power

supply. Capacitors of suitable values can be connected at input and output pins depending upon the

respective voltage levels.

Figure 16 LM7805

3.5.1 Pin description:

Pin No Function Name1 Input voltage (5V-18V) Input

2 Ground (0V) Ground

3 Regulated output; 5V (4.8V-5.2V) Output

Table 3

3.5.2 Circuit:

Proper operation requires a common ground between input and output voltages. The difference

between input and output voltages is called dropout voltage. Acapacitorof 0.33µF is required if the

regulator is located at an appreciable distance from the power supply filter. Even though capacitor of

0.1 µF is not needed, it may be used to improve the transient response of the regulator.

Figure 17 LM7805 Connection Diagram


33/52

33

3.6 LM317

The LM317 is an adjustable 3−terminal positive voltage regulator capable of supplying in excess of 1.5

A over an output voltage range of 1.2 V to 37 V. This voltage regulator is exceptionally easy to use and

requires only two external resistors to set the output voltage. Further, it employs internal current

limiting, thermal shutdown and safe area compensation, making it essentially blow−out proof. The

LM317 serves a wide variety of applications including local, on card regulation. This device can also

be used to make a programmable output regulator, or by connecting a fixed resistor between the

adjustment and output, the LM317 can be used as a precision current regulator.

Figure 18 LM317

3.6.1 Features

Output Current in Excess of 1.5 A

Output Adjustable between 1.2 V and 37 V

Internal Thermal Overload Protection

Internal Short Circuit Current Limiting Constant with Temperature

Output Transistor Safe−Area Compensation

Floating Operation for High Voltage Applications

Available in Surface Mount D2PAK −3, and Standard 3−Lead

Transistor Package

NCV Prefix for Automotive and Other Applications Requiring

Unique Site and Control Change Requirements; AEC−Q100

Qualified and PPAP Capable

Eliminates Stocking many Fixed Voltages

These are Pb−Free Devices


34/52

34

3.6.2 Circuit

Figure 19 LM317 Circuit Diagram

*_Cin is required if regulator is located an appreciable distance from power supply filter.

**_CO is not needed for stability, however, it does improve transient response.

Since IAdj is controlled to less than 100 A, the error associated with this term isnegligible in most applications.


35/52

35

CHAPTER 4

ASSEMBLING THE ROBOT

Block Diagram for balancing robot

Chassis


36/52

36

Tyres

Assembling the motors with chassis


37/52

37

Assembling the wheels

Circuit designing on PCB


38/52

38

Final Structure


39/52

4.1 AVR STUDIO

AVR Studio is used by embe

Atmel microprocessors such

assembly programming for th

debugging. Beginning with v

readily used in conjunction wi

All Atmel AVR microcontrol

software, you can use an inteIDE contains everything you

your code straight into the on

components.

STEPS TO PROGRA

1. Open AVR Studio and c

project name and initia

“BlinkLED” and elected

file “BlinkLED.c”. Click

“Finish”, you will not b

going to the “Project” me

Figu

39

CHA

PROGRAMMING THE

ded programmers for programming and debugging for

as the Atmega8 or even the Atmega128. While it has

ose who prefer to use higher languages, it uses the cof

ersion 4 AVR Studio has now moved to dwarf2, and c

th the open source gcc based compiler WinAVR.

lers require some software to be useful. To create and

rated development environment (IDE), such as Atmeleed to create, compile and debug code, and it will let yo

chip Flash of the AVR microcontroller - without any ot

lick New Project. Select AVR GCC for the project typ

l file name. In the screenshot below, we named

to have a folder called “C:\BlinkLED” created containi

Next >>. DO NOT click “Finish” yet. If you do accid

able to perform step 2 and will instead have to set th

u and selecting “Configuration Options”.

re 20 Creating new AVR Studio-4 project

PTER 5

OBOT

any of the

support for

format for

an be more

debug this

tudio. Thisu download

er software

. Enter the

our project

g the blank

ntally click

e device by


40/52

2. Select AVR Simulator a

target AVR. For an Ora

ATmega328P, ATmega3

your Orangutan or 3pi Ro

Figu

3. Write your program in Bl

on the toolbar (or press F

Figu

40

the debug platform and then select the appropriate dev

gutan or 3pi Robot, this will either be ATmega48,

24PA, ATmega644P, or ATmega1284P depending on

bot has. Click Finish.

re 21 Creating new AVR Studio-4 Project

nkLED.c as seen in the screen shot below and click the

).

re 22 Building a project with AVR Studio

ce for your

Tmega168,

which chip

uild button


41/52

4. Make sure your USB AV

B cable and then click th

accomplish this by going

Figure 23

5. This will bring up a prog

AVR programmer uses A

this is not the same as A

what it is, or select Auto

You can determine your p

of your Device Manage

“Connect…” to bring up t

Figure 24 A

If the ISP window does n

the programmer. Please s

If AVR Studio brings

programmer’s firmware,

prevent this dialog from

programmer’s hardware a

41

programmer is connected to your computer via its US

Display the ‘Connect’ Dialog button on the toolbar. Y

to the “Tools” menu and selecting Program AVR > Con

onnecting to the programmer with AVR studio

rammer selection dialog. Select AVRISP as the platfor

VR ISP version 2, which is written as AVRISPv2. Plea

R ISP mkII. Select the port name of your programmer i

nd AVR Studio will try all the ports unti l it detects the p

rogrammer’s port name by looking in the “Ports (COM

for “Pololu USB AVR Programmer Programming

he ISP window.

VR Studio-4's programmer selection dialog box

ot appear when you click “Connect…”, your computer c

e Troubleshooting for help identifying and fixing the pro

p a dialog asking if you want to upgrade (or down

click Cancel to ignore the message and use your prog

appearing in the future, use the Configuration Utilit

nd software version numbers.

A to mini-

ou can also

nect….

. The USB

se note that

f you know

rogrammer.

LPT)” list

ort”. Click

nnot detect

blem.

rade) your

ammer. To

change the


42/52

6. Select the Main tab. In t

you selected when you c

be ATmega48, ATmega1

Figure

7. If you have not done so

ISP cable. Make sure the

your target device! You

the Read Signature butt

signature. If everything

If the signature does no

selected (or possibly you

please see Troubleshootin

Figure 26 Readi

8. Now it is time to progr

File in the Flash section program. You can brows

If you navigate to your p

Click the Program butto

“EEPROM” or “ELF Pro

42

e dropdown box that lists AVR models, select the same

reated the project. For an Orangutan or 3pi Robot, this

68, or ATmega328P.

25Selecting the device for ISP programming

lready, connect the programmer to the target device usi

cable is oriented so that pin 1 on the connector lines up

can test the connection by going to the Main tab a

n. This sends a command to the target AVR asking fo

orks correctly, you should see “Signature matches selec

match the selected device, you probably have the w

r target device is turned off). If reading the signature f

g for help getting your connection working.

ng the device signature in AVR studio's main ISP tab

m your target device. Select the Program tab. Your I

needs to be the hex file that was generated when yofor this using the "..." button to the right of the input fi

oject’s folder, you should find it as “default\.hex”.

one in the


43/52

F

43

igure 27 AVR Studio's program ISP tab


44/52

44

CHAPTER 6

TESTING THE ROBOT

Prior to programming the robot with final program, several test programs were burned in the

microcontroller to verify various discrete segments of the hardware structure. The robot was tested to

verify the functioning of the following segments for proper operation:

Regulated 5V dc output from LM7805

Regulated 3.13V dc power output from LM317 regulator.

Level conversion from bidirectional level converter.

PID control unit

Motor assembly

Sensor Check

6.1. PID Control unit

Working of PID control unit was tested by varying the brightness of an LED connected at pin no.

16 of the microcontroller. Whenever the control knob of potentiometer is rotated, the brightness

of the LED varies from maximum to off state. This indicated the proper functioning of the PID

unit.

Code for PID Check:

int Led=10;

intanalog=A1;

intval=0;

void setup()

{

pinMode(Led, OUTPUT);

pinMode(analog, INPUT);


45/52

45

}

void loop()

{

val = analogRead(analog);

analogWrite ( Led, val/4);

}

6.2. Motor Speed Control

While balancing the robot on two wheels, it is necessary to adjust the speed of the motor in

accordance with instability of the system. If the system is highly unstable i.e. far away from it’s

balanced position, then the motor should work on higher speed.

But if the robot is slightly misaligned, then the speed of the motor should be slow. In case the

motor provides the same higher speed, robot may encounter a jerk in the opposite direction and

thus result in imbalance in the other direction.

The speed of the motor is controlled by using analog output from PID unit through

potentiometers. The PWM pins of microcontroller are connected to enable pins of motor driver(

pins 1 and 9).

Code for Speed Check:

int Led=10;

intanaloga=A1;

int Motor1a = 0;

int Motor1b = 1;

int Motor2a = 2;

int Motor2b = 3;

intMotorEnable=5;

int MotorEnable2=6;

intanalog=A2;

intval=0;

voidMotorSetup()

{

pinMode(Motor1a, OUTPUT);


46/52

46

pinMode(Motor1b, OUTPUT);

pinMode(Motor2a, OUTPUT);

pinMode(Motor2b, OUTPUT);

}

voidmForward()

{

digitalWrite(Motor1a, HIGH);

digitalWrite(Motor1b, LOW);

digitalWrite(Motor2a, HIGH);

digitalWrite(Motor2b, LOW);

}

void setup()

{

pinMode(Led, OUTPUT);

pinMode(analoga, INPUT);

pinMode(MotorEnable, OUTPUT);

pinMode(analog, INPUT);

pinMode(MotorEnable2, OUTPUT);

digitalWrite(MotorEnable2, LOW);

MotorSetup();

mForward();

}

void loop()

{

val = analogRead(analoga);

analogWrite ( Led, val/4);

// val = analogRead(analog);

analogWrite ( MotorEnable, val/4);

}


47/52

47

6.3. Sensor Check

Output from MPU6050 was observed. Movement of the robot in three axis resulted in change of

current coordinates which indicated that the sensor was properly connected and performing the

desired operation.

Sensor check code:

// I2Cdev and MPU6050 must be installed as libraries, or else the .cpp/.h files

// for both classes must be in the include path of your project

#include "I2Cdev.h"

#include "MPU6050.h"

// Arduino Wire library is required if I2Cdev I2CDEV_ARDUINO_WIRE implementation

// is used in I2Cdev.h

#if I2CDEV_IMPLEMENTATION == I2CDEV_ARDUINO_WIRE

#include "Wire.h"

#endif

// class default I2C address is 0x68

// specific I2C addresses may be passed as a parameter here

// AD0 low = 0x68 (default for InvenSense evaluation board)

// AD0 high = 0x69

MPU6050 accelgyro;

//MPU6050 accelgyro(0x69); //


48/52

48

// for a human.

//#define OUTPUT_BINARY_ACCELGYRO

#define LED_PIN 13

boolblinkState = false;

void setup()

{

// join I2C bus (I2Cdev library doesn't do this automatically)

#if I2CDEV_IMPLEMENTATION == I2CDEV_ARDUINO_WIRE

Wire.begin();

#elif I2CDEV_IMPLEMENTATION == I2CDEV_BUILTIN_FASTWIRE

Fastwire::setup(400, true);

#endif

// initialize serial communication

// (38400 chosen because it works as well at 8MHz as it does at 16MHz, but

// it's really up to you depending on your project)

Serial.begin(38400);

// initialize device

Serial.println("Initializing I2C devices...");

accelgyro.initialize();

// verify connection

Serial.println("Testing device connections...");

Serial.println(accelgyro.testConnection() ? "MPU6050 connection successful" : "MPU6050

connection failed");

// use the code below to change accel/gyro offset values

/*

Serial.println("Updating internal sensor offsets...");

// -76 -2359 1688 0 0 0

Serial.print(accelgyro.getXAccelOffset()); Serial.print("\t"); // -76

Serial.print(accelgyro.getYAccelOffset()); Serial.print("\t"); // -2359

Serial.print(accelgyro.getZAccelOffset()); Serial.print("\t"); // 1688


49/52

49

Serial.print(accelgyro.getXGyroOffset()); Serial.print("\t"); // 0

Serial.print(accelgyro.getYGyroOffset()); Serial.print("\t"); // 0

Serial.print(accelgyro.getZGyroOffset()); Serial.print("\t"); // 0

Serial.print("\n");

accelgyro.setXGyroOffset(220);

accelgyro.setYGyroOffset(76);

accelgyro.setZGyroOffset(-85);

Serial.print(accelgyro.getXAccelOffset()); Serial.print("\t"); // -76

Serial.print(accelgyro.getYAccelOffset()); Serial.print("\t"); // -2359

Serial.print(accelgyro.getZAccelOffset()); Serial.print("\t"); // 1688

Serial.print(accelgyro.getXGyroOffset()); Serial.print("\t"); // 0

Serial.print(accelgyro.getYGyroOffset()); Serial.print("\t"); // 0

Serial.print(accelgyro.getZGyroOffset()); Serial.print("\t"); // 0

Serial.print("\n");

*/

// configure Arduino LED for

pinMode(LED_PIN, OUTPUT);

}

void loop()

{

// read raw accel/gyro measurements from device

accelgyro.getMotion6(&ax, &ay, &az, &gx, &gy, &gz);

// these methods (and a few others) are also available

//accelgyro.getAcceleration(&ax, &ay, &az);

//accelgyro.getRotation(&gx, &gy, &gz);

#ifdef OUTPUT_READABLE_ACCELGYRO

// display tab-separated accel/gyro x/y/z values

Serial.print("a/g:\t");

Serial.print(ax); Serial.print("\t");

Serial.print(ay); Serial.print("\t");


50/52

50

Serial.print(az); Serial.print("\t");

Serial.print(gx); Serial.print("\t");

Serial.print(gy); Serial.print("\t");

Serial.println(gz);

#endif

#ifdef OUTPUT_BINARY_ACCELGYRO

Serial.write((uint8_t)(ax>> 8)); Serial.write((uint8_t)(ax& 0xFF));

Serial.write((uint8_t)(ay >> 8)); Serial.write((uint8_t)(ay & 0xFF));

Serial.write((uint8_t)(az>> 8)); Serial.write((uint8_t)(az& 0xFF));

Serial.write((uint8_t)(gx>> 8)); Serial.write((uint8_t)(gx& 0xFF));

Serial.write((uint8_t)(gy>> 8)); Serial.write((uint8_t)(gy& 0xFF));

Serial.write((uint8_t)(gz>> 8)); Serial.write((uint8_t)(gz& 0xFF));

#endif

// blink LED to indicate activity

blinkState = !blinkState;

digitalWrite(LED_PIN, blinkState);

}


51/52

51

Output Graphs:


52/52

BIBLIOGRAPHY

http://www.instructables.com/id/2-Wheel-Self-Balancing-Robot-by-using-Arduino-and-/

http://en.wikipedia.org/wiki/Kalman_filter

http://forum.arduino.cc/index.php?topic=58048.0

http://www.instructables.com/id/PCB-Quadrotor-Brushless/step15/IMU-Part-

2-Complementary-Filter/

http://en.wikipedia.org/wiki/PID_controller

http://www.atmel.in/devices/ATMEGA328P.aspx

http://www.invensense.com/mems/gyro/mpu6050.html

http://playground.arduino.cc/Main/I2CBi-directionalLevelShifter

http://www.ti.com/lit/ds/symlink/l293d.pdf

http://www.ti.com/lit/ds/symlink/lm317.pdf

http://www.engineersgarage.com/tutorials/avr-studio4-working

http://stackoverflow.com/tags/avr-studio4/info

http://playground.arduino.cc/Main/I2CBi-directionalLevelShifter

http://www.atmel.in/tools/atmelstudio.aspx

balancing robot atmega328

Documents