crane control using accelerometer


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hand gesture based crane control using accelerometer

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crane control using accelerometer

  1. 1. CONTENTS • • • • • • • • • • • 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
  2. 2. 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.
  3. 3. 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.
  4. 4. 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
  5. 5. •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
  6. 6. 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
  7. 7. 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.
  8. 8. 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  
  9. 9. MOTOR DRIVER - L293D
  10. 10. 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
  11. 11. 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
  14. 14. 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. 16. 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.
  17. 17. 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
  18. 18. Thank you
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