Difference between revisions of "S16: SkyNet"

Revision as of 05:33, 23 May 2016

Grading Criteria

SkyNet

Meeting notes

This section is temporary and will only exist on the wiki during development.

   C++ for OpenCV
       Have target stand dead center and let the PC choose what to target
           - Green for go 
           - red for lost
   Graceful halt, error compensation in OpenCV layer

   API for % speed for x and y axis
   x_axis_speed(int speed_percent)
   y_axis_speed(int speed_percent)
   "Dumb motor system" should not know about error or destination, only knows how fast to go in a direction

   Visible framework
   3D printing
   Autodesk Fusion 360

   - Completed Design for PCB
   - Initial quote was 18 per board
   - Looking into the price and seeing other fab house prices
   - Aiming to get PCB in 2 weeks time

   - Going to use TI controller as base
   - Fallback is Servo Motors
   - Motors used in robotic arms? slow and percise
   - Will be using EVM from TI to test out TI behavior
   - Looking into other controllers to get desired result L6234, SPWM signals (Sine-wave PWM)

   - Re-adjust Raspberry Pi Cubby on Horizontal Frame
   - Add more support to Horizontal Frame arms
   - Aiming to get test print by next meeting, test durability.

   - Trained model for people detection already exists
       - How to differentiate a person? Premade function gives back a list of everyone
       - We CAN detect people, We need to find out how to narrrow down the targets
           - Use rectangles to get coordinates combined with HSV
           - Combine coordinates, analyze with HSV
   - HAR face tracking
       - possibly can single out a target

   Project Frame
       - Successfully printed out Horizontal frame, approximately 9 hours to complete
           - Used a SLA converter to get SLA file from 3D model in Fusion 360
           - Used a slicer tool to create a sliced version of the model to use in the 3D printer 
       - Successfully drove a motor slow
           - Used method detailed in this article http://www.berryjam.eu/2015/04/driving-bldc-gimbals-at-super-slow-speeds-with-arduino/
   Raspberry Pi PWM
       - Found two libraries to implement PWM control
   Circuit diagram
       - Started on full circuit diagram

   New ways of tracking
       - Used frame difference tracking
       - Pattern tracking
   Template Matching and frame difference
       - Look for frame differences and then choose objects based on the different objects in a frame
       - Use template matching to then track the object that was choosen
   Raspberry Pi Camera implementation
       - Developed on laptop first and then moved to Raspberry Pi
       - Heating issues on the Raspberry Pi
       - Really bad performance capture at 20-30 frames
       - Will get metrics of performance in next run
       - Reduce the 
   Template Matching
       - It can dictate anything as an object
           - car, hand, etc.
       - We have to select it to target
       - Fast moving objects were still trackable

   Stepper motor control
       - Accurate because of the way the motor is built
       - Motors can be bought at various steps per rotation
       - Microstepping can increase the amount of steps per rotation
   Why switch from brushless motors?
       - Brushless motor control is too inaccurate for camera control
       - Voltage/current draw too large while using brushless
   Power circuit
       - Circuit to supply the power to the system
       - The motors themselves have a max draw about 1 amp
       - Raspberry PI needs minimum 5V and at max 1A
       - Development phase use a 12V 3A wall adapter
       - If time permits run system on battery power

   Template Matching inadequate 
       - Takes too much processing power
   Machine Learning Training
       - Offline training
       - Online training (Real time training)
   Fallback HSV tracking
       - Will develop until it can be feasibly used
   Min spec 
       - Tracking a student walking across the classroom

Abstract

SkyNet is a tracking tripod mount that will follow a given target using computer vision technologies. The system utilizes two brushless motors that are controlled by inputs given from a Raspberry Pi 3. The Raspberry Pi 3 utilizes the OpenCV open source library to calculate the deviation of a tracked object from the center of its view. It will then control the motors to correct the camera position such that the target will always be in the center of the video. The mount will be able to hold any standard 5-inch phone (should aim for universal mount) for video recording.

Objectives & Introduction

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Team Members & Responsibilities

• Steven Hwu
  • OpenCV
• Jason Tran
  • OpenCV
• Andrew Herbst
  • Motor system
• Vince Ly
  • Motor system

Schedule

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Parts List & Cost

ECU:

   RaspBerry Pi 3 Rev B ~$40
       - https://www.raspberrypi.org/products/raspberry-pi-3-model-b/

Tracking Camera:

   Raspberry Pi Camera Board Module ~$20
       - http://www.amazon.com/Raspberry-5MP-Camera-Board-Module/dp/B00E1GGE40

Stepper Motors:

   Nema 14 Stepper Motor (Pitch Motor)~$24
       - http://www.amazon.com/0-9deg-Stepper-Bipolar-36x19-5mm-4-wires/dp/B00W91K3T6/ref=pd_sim_60_2?ie=UTF8&dpID=41CZcFZwJJL&dpSrc=sims&preST=_AC_UL160_SR160%2C160_&refRID=0ZZRP157RFT10QDHHKN4

   Stepping Motor Nema 17 Stepping Motor (Yaw Motor) ~$13
       - http://www.amazon.com/Stepping-Motor-26Ncm-36-8oz-Printer/dp/B00PNEQ9T4/ref=sr_1_4?s=industrial&ie=UTF8&qid=1463981462&sr=1-4&keywords=stepper+motor+nema+17

Controllers:

   x2 DRV8825 Stepper Motor Driver Carrier, High Current ~$10
       - https://www.pololu.com/product/2133

Phone mount:

  x1 Tripod phone mount ~$5

Whole Enclosure:

   3D printed

Total: ~$123

Design & Implementation

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Hardware Design

A custom frame was created to hold the motors and cameras in place. The initial design of the system was to use brushless motors to control the motion of the camera as an object was being tracked. This proved to be difficult because brushless motors have very high KV (rpm constant). This value is RPM/Volt, essentially a ratio to convert voltage to RPM. The motor initially being used was a motor rated at 70 KV.

Notes from 4/4

Other motors are being considered for this project which include servos and stepper motors. Servo motors have the problem of being too jittery/abrupt which is not ideal for recording video, this would defeat the purpose of the camera system. Stepper motors are also not ideal due to the fact that they are very heavy and require a large frame for the system to be structurally sound. Experiments will continue to verify which motor is ideal for the task.

Figure 1. Power Supply Unit

Power Unit:

A +12V supply was needed to power both of the motor drivers. A +12V wall adapter was used along with a pair of coupling capacitors to run power to the DRV8825 IC's. In addition, a +5V supply was required to power the Raspberry Pi 3 board, which controls the motor drivers over GPIO. To step down the 12 volts from the wall adapter, we used a 5V switching regulator because it could handle the heat and power capacity that would be required when running the motors.

Motor Driver:

Figure 2. TI DRV8825 Motor Driver

The TI DRV8825 IC is capable of driving a bipolar stepper motor, so two of these chips were implemented for the pitch and yaw motors. The controller was powered with a 12V supply, as shown in the figure. This specific motor driver was chosen primarily because of its simple STEP/DIR interface. The driver would receive a direction signal along with a PWM signal. The frequency of the PWM signal dictates the speed at which the stepper motor would turn in the direction specified by the DIR pin. The motor driver also had microstepping capabilities so that we could coordinate the steps per rotation of each motor to deliver the maximum smoothness while turning the motor. The breakout board that carried the motor driver allowed for easy implementation and connection to the Raspberry Pi and PWM driver.

PWM Driver:

Figure 3. PCA9685 I2C Bus PWM Controller

Because the Raspberry Pi 3 only has one hardware PWM output pin, an external PWM driver was chosen to drive the PWM signals that controlled the two stepper motors. The PCA9685 16-channel PWM controller was chosen as the interface between the microcontroller and the two stepper motors. The PWM controller communicates with the Raspberry Pi over the I2C bus with just two wires. With this device, the PWM frequency could be adjusted between 24Hz and 1526 Hz, which was an acceptable range for the application of this project. The frequency was set by modifying the register that dictated the frequency prescaler in the equation in figure 4.

Figure 4. PWM Frequency Calculation

Hardware Interface

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Figure 5. Code Hierarchy and Interfacing

Motor System Class:

In order to communicate from the higher-level OpenCV motion tracking module to the lower-level motors, a Motor System class was developed in the intermediate layer. The Motor System class was developed with various functions that the motion tracking module would call upon to control the individual motors' movement. A summary of the basic functions the Motor System provided are below. The main purpose of the Motor System is to pass the speed and direction commanded by the OpenCV module to the individual motors and also to determine the motor step size that would produce the most refined movement of the motors.

// Motor System Class

    void power_off(void);

    void power_on(void);

    /**
     * Sets the speed for both the yaw_motor and pitch motor
     *
     * @param x_speed speed of horizontal motor
     * @param y_speed speed of vertical motor
     */
    void set_x_y_speed(float x_speed, float y_speed);

    /**
     * Checks if either system is faulted
     *
     * @return true if either motor is faulted
     */
    bool is_faulted();

    /*
    * Sets motor step size for maximum smoothness given minimum rotation time
    */
    void set_smoothness();

The Motor System is also responsible through its constructor for initializing the pitch motor and yaw motor by assigning each GPIO pin to its corresponding function in the Driver class.

// Motor Initialization: 

    pitch_motor = new Motor_driver(PITCH_PHYSICAL_STEPS, PWM_0, PIN_2, PIN_3, PIN_21, PIN_22, PIN_23, PIN_24, PIN_0);
    yaw_motor   = new Motor_driver(YAW_PHYSICAL_STEPS, PWM_1, PIN_4, PIN_5, PIN_6, PIN_25, PIN_27, PIN_28, PIN_1);

Motor Driver Class:

The Motor Driver class has two instances, one for each motor, which is passed information such as speed and direction from the Motor System functions. Below is a list of the important functions available to the Motor Driver, the most vital being the rotate() function which parses the x and y speed information from the Motor System class and sets the corresponding DIR pin and sets the frequency of the PWM signal to send to the motor to control speed.

// Motor Driver Class

     /**
     * Sets the rotation of the motor can either be
     * NEGATIVE, POSITIVE, or HOLD
     *
     * @param motion    specifies the type of motion
     * @param speed     Sets the speed of the motor, 0 <= speed >= 1000
     */
    void rotate(motion_e motion, int speed);
    
    /** 
     * Sets the step size of the motor
     *
     * @param step_size_e step size for motor
     */
    void set_step_size(step_size_e step_size);
     
    /**
     * Sets the direction of the motor positive or negative
     *
     * @param motion    direction to move
     */ 
    void set_dir(motion_e motion);
    
    /**
     * Holds the current position motor is at, used for speed == 0
     *
     */
    void hold_position(void);

When the motors are initialized, the constructor carries out the following tasks:

   1. Set steps per rotation
   2. Set current motion to HOLD
   3. Set step size to 1
   4. Set current speed to zero
   5. Initialize the control pins of the motor driver to correspond to the GPIO pins of the Raspberry Pi

PWM Driver:

The Raspberry Pi communicates with the PCA9685 PWM driver over the I2C bus through two wires, SDA and SCL. The microcontroller can manipulate the motor drivers by writing to different control registers on the PCA9685 in order to configure the output PWM and duty cycle to the motor drivers. For example, the sequence to set an output PWM frequency is as follows:

   1. Check that the requested frequency is within the boundary (24-1526 Hz) and clamp value if necessary
   2. Calculate prescaler based on requested frequency using formula from figure 4
   3. Set sleep mode bit by writing 0x10 to Mode 1 address 0x00
   4. Write the calculated prescaler value to the prescaler register at address 0xFE
   5. Clear the sleep bit by writing 0x00 to Mode 1 address 0x00

Software Design

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Implementation

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Tracking Application using Haar Cascade Object Detection

The top level software using Haar Cascade object detection consists of 3 major steps:

1. Initialization
   • Create a classifier object, then load a classifier file
     • A classifier is an XML file that describes a particular object, for example, the full body of a person.
   • Initialize the video source
     • The video source is an object that represents the video capture device, for example, a USB camera. Real-world visual data as a matrix can be obtained from this object.
2. Capture an instance from the video source (also known as a frame), then perform object detection on it
   • Object detection is performed by searching the given frame for objects that match the previously loaded classifier file.
   • After analyzing, a list of found objects is returned; found objects are represented as Rectangle objects.
3. Determine if the tripod’s camera needs to be rotated based on the target’s location in the frame.
   • Using the first detected object in the objects found list as the target, determine whether the tripod should pan or tilt.
   • Currently, the tripod has the capability to pan/tilt in the following directions:
     • Left
     • Right
     • Up
     • Down
   • If the panning/tilting action is required, activate the appropriate stepper motors (x-axis or y-axis)

Testing & Technical Challenges

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My Issue #1

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Conclusion

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Project Video

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Project Source Code

• Sourceforge Source Code Link

References

Acknowledgement

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References Used

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Appendix

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