Difference between revisions of "S16: SkyNet"

Revision as of 20:55, 22 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
  • Brushless Motor system
• Vince Ly
  • Brushless Motor system

Schedule

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Parts List & Cost

ECU:

   RaspBerry Pi 3 Rev B

Tracking Camera:

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

Brushless Motors:

   Nema 14 Stepper 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

Controllers:

   DRV8711 Stepper Motor Controller IC
       - http://www.ti.com/lit/ds/symlink/drv8711.pdf

Phone mount:

   Vince's cheap ass mount ~Free

Whole Enclosure:

   3D printed

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.

Hardware Interface

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Software Design

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Tracking 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 horizontally or vertically.
   • Currently, the tripod has the capability to pan in the following directions:
     • Left
     • Right
     • Up
     • Down
   • If the panning action is required, activate the appropriate stepper motors

Implementation

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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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