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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 Technology Guru Nanak Dev University Amritsar – 143005 India 1

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:

2

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.

3

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.

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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 5

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

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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 Kp (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............................................................................................................................24 11. 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

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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 sensor fusion. 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.

8

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 low frequency 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

9

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.

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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

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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.

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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.

13

2.3 PID Controller

A proportional-integral-derivative derivative mechanism (controller))

widely

controller (PID used

in industrial

controller) control

is

a control

loop feedback

systems (Programmable Programmable

Logic

Controllers, SCADA systems, Remote Terminal Units etc). A PID controller calculates an "error" value as the difference between a measured process variable and a desired set point.. The controller attempts to minimize the error in outputs by adjusting the process control inputs. The PID controller algorithm involves involves three separate constant parameters, and is accordingly sometimes

called three-term term

control control:

the proportional,

the integral and derivative values,

denoted P, I, and D. Simply put, these values can be interpreted in terms of time: P depends on the present error, I on the accumulation of past errors, and D is a prediction of future errors, based on current rate of change. The weighted sum of these three actions is used to adjust the process via a control element such as the position of a control valve, a damper,, or the power supplied to a heating element. In the absence of knowledge of the underlying process, a PID controller has historically been considered to be the best controller. By tuning the three parameters in the PID controller algorithm, the controller can provide control action designed for specific process requirements. The response of the controller troller can be described in terms of the responsiveness of the controller to an error, the degree to which the controller overshoots the set point, and the degree of system oscillation. Note that the use of the PID algorithm for control does not guarantee optimal control of the system or system stability.

Figure 3 PID controller block diagram

Some applications may require using only one or two actions to provide the appropriate system control. This is achieved by setting the other parameters to zero. A PID controller will be called a PI, PD, P or I controller in the absence of the respective control actions. PI controllers are fairly common, since derivative action is sensitive to measurement noise, whereas the absence of an integral integral term may prevent the system from reaching its target value due to the control action.

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2.3.1 PID controller theory

The PID control scheme is named after its three correcting terms, whose sum constitutes the manipulated variable (MV). The proportional, integral, and derivative terms are summed to calculate the output of the PID controller. Defining

as the controller output, the final form of the PID

algorithm is:

where : Proportional gain, a tuning parameter : Integral gain, a tuning parameter : Derivative gain, a tuning parameter : Error : Time or instantaneous time (the present) : Variable of integration; takes on values from time 0 to the present

.

SP: Set point PV: Process Variable

Proportional Term The proportional term produces an output value that is proportional to the current error value. The proportional response can be adjusted by multiplying the error by a constant Kp, called the proportional gain constant. The proportional term is given by:

A high proportional gain results in a large change in the output for a given change in the error. If the proportional gain is too high, the system can become unstable. In contrast, a small gain results in a small output response to a large input error, and a less responsive or less sensit sensitive ive controller. If the proportional gain is too low, the control action may be too small when responding to system disturbances. Tuning theory and industrial practice indicate that the proportional term should contribute the bulk of the output change.

15

Figure 4Plot Plot of PV vs time, for three values of Kp (Ki and Kdheld constant)

Integral Term The contribution from the integral term is proportional to both the magnitude of the error and the duration of the error. The integral in a PID controller is the sum of the instantaneous error over time and gives the accumulated offset that should have been corrected previously. The accumulated error is then multiplied ed by the integral gain (

) and added to the controller output.

The integral term is given by:

The integral term accelerates the movement of the process towards set point and eliminates the residual steady-state state error that occurs with a pure proportional controller. However, since the integral term responds to accumulated errors from the past, it can cause the present value to overshoot the set point value

Figure 5Plot Plot of PV vs time, for three values of Ki (Kp and Kdheld constant)

16

Derivative Term The derivative of the process error is calculated by determining the slope of the error over time and multiplying this rate of change by the derivative derivat gain Kd. The magnitude of the contribution of the derivative term to the overall control action is termed the derivative gain, Kd. The derivative term is given by:

Derivative action predicts system behaviour and thus improves settling time and stabil stability of the system. An ideal derivative is not causal, so that implementations of PID controllers include an additional low pass filtering for the derivative term, to limit the high frequency gain and noise. Derivative action is seldom used in practice though thou - by one estimate in only 20% of deployed controllers - because of its variable impact on system stability in real real-world applications.

Figure 6Plot Plot of PV vs time, for three values of Kd (Kp and Kiheld constant)

2.3.2 Limitations of PID controller While PID controllers are applicable to many control problems, and often perform satisfactorily without any improvements or only coarse tuning, they can perform poorly in some applications, and do not in general provide optimal control. control. The fundamental difficulty with PID control is that it is a feedback system, with constant parameters, and no direct knowledge of the process, and thus overall performance is reactive and a compromise. While PID control is the best controller in an observer without a model of the process, better performance can be obtained by overtly modelling the actor of the process without resorting to an observer. PID controllers, when used alone, can give poor performance when the PID loop gains must be reduced so that the control system does not overshoot, oscillate or hunt about the control set point value. They also have difficulties in the presence of non-linearities, non may trade-off off regulation versus response time,

17

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.

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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

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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

20

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

21

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 is unprogrammed, 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.

22

3.1.3 FEATURES Atmel

Manufacturer: 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 Bit Size:

10 bit

A/D Channels Available:

8

Brand:

Atmel

Data Ram Type:

SRAM

Data ROM Size:

1 kB

Data Rom Type:

EEPROM

Interface Type:

I2C, SPI, USART

Minimum Operating Temperature:

- 40 C

Number of Programmable 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

23

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.

24

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 I2C 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 Features The 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 userprogrammable 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

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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)

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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.

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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.

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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

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3.4.2 Pin Diagram:

Figure 14 L293D Pin Diagram

Pin Description:

Table 2

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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

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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 1 2 3

Function Input voltage (5V-18V) Ground (0V) Regulated output; 5V (4.8V-5.2V) Table 3

Name Input Ground Output

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

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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

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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 is negligible in most applications.

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CHAPTER 4 ASSEMBLING THE ROBOT

Block Diagram for balancing robot

Chassis

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Tyres

Assembling the motors with chassis

36

Assembling the wheels

Circuit designing on PCB

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Final Structure

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CHAPTER 5 PROGRAMMING THE ROBOT

4.1 AVR STUDIO AVR Studio is used by embedded programmers for programming and debugging for many of the Atmel microprocessors such as the Atmega8 or even the Atmega128. While it has support for assembly programming for those who prefer to use higher languages, it uses the coff format for debugging. Beginning with version 4 AVR Studio has now moved to dwarf2, and ccan be more readily used in conjunction with the open source gcc based compiler WinAVR. All Atmel AVR microcontrollers require some software to be useful. To create and debug this software, you can use an integrated development environment (IDE), such as Atmel Studio. This IDE contains everything you need to create, compile and debug code, and it will let you download your code straight into the on--chip Flash of the AVR microcontroller - without any other software components.

STEPS TO PROGRAM 1.

Open AVR Studio io and click New Project. Select AVR GCC for the project type. Enter the project name and initial file name. In the screenshot below, we named our project “BlinkLED” and elected to have a folder called “C:\BlinkLED” “C: BlinkLED” created containing the blank file “BlinkLED.c”. Click Next >>. DO NOT click “Finish” yet. If you do accidentally click “Finish”, you will not be able to perform step 2 and will instead have to set the device by going to the “Project” menu and selecting “Configuration Options”.

Figure 20 Creating new AVR Studio-4 project

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2.

Select AVR Simulator as the debug platform and then select the appropriate device for your target AVR. For an Orangutan or 3pi Robot, this will either be ATmega48, ATmega168, ATmega328P, ATmega324PA, 24PA, ATmega644P, or ATmega1284P depending on which chip your Orangutan or 3pi Robot has. Click Finish.

Figure 21 Creating new AVR Studio-4 Project

3.

Write your program in BlinkLED.c as seen in the screen shot below and click the B Build button on the toolbar (or press F7). F7

Figure 22 Building a project with AVR Studio

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4.

Make sure your USB AVR programmer is connected to your computer via its USB A to mini miniB cable and then click the Display the ‘Connect’ Dialog button on the toolbar. You can also accomplish this by going to the “Tools” menu and selecting Program AVR > Connect… Connect….

Figure 23 Connecting to the programmer with AVR studio

5.

This will bring up a programmer selection dialog. Select Sele AVRISP as the platform. The USB AVR programmer uses AVR ISP version 2, which is written as AVRISPv2. Please note that this is not the same as AVR ISP mkII. Select the port name of your programmer if you know what it is, or select Auto and AVR Studio will try all the ports until it detects the programmer. You can determine your programmer’s port name by looking in the “Ports (COM & LPT)” list of your Device Manager for “Pololu USB AVR Programmer Programming Port”. Click “Connect…” to bring up the ISP w window.

Figure 24 AVR Studio-4's Studio programmer selection dialog box

If the ISP window does not appear when you click “Connect…”, your computer cannot detect the programmer. Please see Troubleshooting for help identifying and fixing the the problem. If AVR Studio brings up a dialog asking if you want to upgrade (or downgrade) your programmer’s firmware, click Cancel to ignore the message and use your programmer. To prevent this dialog from appearing in the future, use the Configuration Utilitychange Utilitychange the programmer’s hardware and software version numbers.

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6.

Select the Main tab. In the dropdown box that lists AVR models, select the same device that you selected when you created the project. For an Orangutan or 3pi Robot, this will either be ATmega48, ATmega168, ATmega168 or ATmega328P.

Figure 25Selecting the device for ISP programming

7.

If you have not done so already, connect the programmer to the target device using the 6-pin 6 ISP cable. Make sure the cable is oriented so that pin 1 on the connector lines up with pin 1 on your target device! You can test the connection by going to the Main tab and clicking the Read Signature button. This sends a command to the target AVR asking for its device signature. If everything works correctly, correctly, you should see “Signature matches selected device”. If the signature does not match the selected device, you probably have the wrong device selected (or possibly your target device is turned off). If reading the signature fails entirely, please see Troubleshooting for help getting your connection working.

Figure 26 Reading the device signature in AVR studio's main ISP tab

8.

Now it is time to program your target device. Select the Program tab. Your Input HEX File in the Flash section needs to be the hex file that was generated when you built your program. You can browse for this using the "..." button to the right of the input file text box. If you navigate to your project’s folder, you should find it as “default\