hand gesture based crane control using accelerometer

2. CONTENTS
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Introduction
Voltage Regulator (7806 – 6V )
ATmega8L ( Microcontroller )
uln2003 and NAND IC (7400)
Motor Driver ( L293D)
Accelerometer ( ADXL-335 ,3 - Axis)
Circuit diagram
Working
Application
Refrences
Conclusion

3. INTRODUCTION
•
Gesture commands freely trainable by the user can be used for
controlling external devices with handheld wireless/wired
sensor unit.
• In this Project we are using accelerometer sensor and its tilt
positions are provided by hand movements
• Consequently the motors will rotate in clockwise or
anticlockwise as to move the Crane head in respective Left ,
Right , Up , Down positions.

4. VOLTAGE REGULATOR
•
7806 voltage regulator is designed to automatically
maintain a constant voltage level .
•
These are found in devices such as
computer power supplier where they stabilize the
DC voltages used by the processor and other
elements.
•
7806 is a member of 78xx series of fixed linear
voltage regulator ICs.
•
7806 provide +6V regulated power supply
Capacitors of suitable values can be connected at
input and output pins depending upon the
respective voltage levels.

5. Atmega8l – MICRO CONTROLLER
•Operating Voltage - 2.7 - 5.5V
•Speed Grades - 0 - 8 MHz
•Power Consumption at 4 Mhz, 3V, 25°C
•Active: 3.6 mA
• Idle Mode: 1.0 mA
• Power-down Mode: 0.5 µa
•High-performance,
•Low-power AVR® 8-bit Microcontroller

6. •8 Kbytes of In-System Programmable Flash with R
ead-W
hileW
rite capabilities.
• 512 bytes of EEPROM.
•1 Kbyte of SRAM.
• 23 general purpose I/O lines
• 32 general purpose working registers
• Three flexible Timer/Counters
• Internal and external interrupts
a serial programmable USART

7. ULN2003
F AT
E URE
S
•500mA rated collector
current(Single output)
•High-voltage outputs: 50V
•Inputs compatible with various
types of logic.
•Relay driver application

8. NAND GATE IC 7400
The output is high when either of
inputs A or B is high, or if neither is
high. In other words, it is normally
high, going low only if both A and B
are high.

9. MOTOR DRIVER - L293D
F
eatures Of M
otor Driver :
•Maximum motor supply voltage: 36V
•Maximum motor supply current: 600 mA per motor
•On-board Heat sink for better performance
•Connectors to connect the I/P pins of the IC to micro controller
•Output current 1A per channel (600 mA for L293D).
•Peak output current 2A per channel ( 1.2A for L293D).
•Inhibit facility.
•High noise immunity.
•Separate logic supply.
•Over temperature protection

10. MOTOR DRIVER - L293D

11. ACCELEROMETER – ADXL-335
F
eatures of Accelerometer
• 3 axis sensing small, low profile package
• 4mm x 4mm x 1.45mm LFCSP low
power:350uA(typical)
• operation: 1.8v to 3.6v 10,000g shock
survival
• excellent temperature stability BW
adjustment with a single capacitor per axis
RoHS/WEEE lead-free complement

12. APPLICATIONS
•Laptop PC: Free-fall Detection
•Cell Phone: Image Stability, Text Scrolling, Motion Dialling, ECompass.
•Pedometer: Motion Sensing
•Portable Handheld: Text Scrolling
•Navigation and Dead Reckoning: E-Compass Tilt Compensation
•Gaming: Tilt and Motion Sensing, Event Recorder
•Robotics: Motion Sensing

13. CIRCUIT DIAGRAM

14. CIRCUIT DIAGRAM ON-BOARD

15. WORKING PRINCIPLE OF
PROJECT
•The basic working principle for Project is passage of the data
signals of accelerometer readings to the Microcontroller.
•The program compiled runs according to the values, which make the
crane function accordingly .
• We have used two axis of three-axis accelerometer. In which, one
axis will control the movement of the head of Crane

16. WORKING OF PROJECT

17. ADVANCEMENT OF PROJECT
We can use the concept of Machine Learning in an advance
part of the project where a pattern of work is learned by the
machine and the goal of machine learning is to program
computers to use example data or past experience to solve a
given problem.
Many successful applications of machine learning exist
already, including systems that analyze past sales data to
predict customer behavior, optimize robot behavior so that a
task can be completed using minimum resources, and extract
knowledge from bioinformatics data.

18. REFRENCE
• S. Waldherr, R. Romero, and S. Thrun, “ A gesture based interface for human-robot
interaction,” in Autonomous Robots, vol. 9, no.2, pp. 151-173, Springer, 2000.
• S. Perrin, A. Cassinelli, and M. Ishikawa, “ Gesture recognition using laser-based tracking
system,” in Sixth IEEE International Conference on Automatic Face and Gesture
Recognition, pp. 541-546, 2004.
• T. Baudel and B.-L. Michel, "Charade: remote control of objects using free-hand
gestures," Co mmun. A , vo l. 36 , pp. 28-35, 1993.
CM
• L. R. Rabiner and B. H. Juang, "An Introduction to Hidden Markov Models," in I
EEE
A
SSP M azine , 1 9 8 6 , pp. 4-1 5.
ag
• Y. Wu and T. S. Huang, "Vision-Based Gesture Recognition:A Review," in Pro ce e ding s
o f the I rnatio nal Ge sture Wo rksho p o n Ge sture -B d Co mmunicatio n in Humannte
ase
Co mpute r I ractio n: Spring e r-Ve rlag , 1 9 9 9 .
nte

19. Thank you