Difference between revisions of "S16: SkyNet"

Revision as of 03:47, 20 April 2016

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SkyNet

Meeting notes

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

Date	Meeting Notes
3/27	Tracking: 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 Motor System: 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 Enclosure: Visible framework 3D printing Autodesk Fusion 360
4/3	+++++++ Meetings Notes ++++++++ PCB Design - 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 Motor controller - 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) CAD Frame - 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. OpenCV - 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
4/10	+++++++ Meetings Notes ++++++++ Hardware Side: 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 Tracking: 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
4/17	+++++++ Meetings Notes ++++++++ Critical Meeting Hardware Side: 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 Tracking: 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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Week#	Due Date	Task	Completed	Notes
1	3/29	- Create Parts list and place order (Motors, Cameras, etc.) - Compile OpenCV C++ code and run examples on Raspberry Pi 3	Completed	- Ordered parts on 3/27 - OpenCV library is building on both development PCs (Steven/Jason) TODO: - Run OpenCV on Raspberry Pi 3
2	4/5	- Create motorized unit - Create the CAD model for 3D printing - Create the breakout board for the motor controller - Be able to track an object in frame (Highlight object)	Completed	- Successfully tracked an object in HSV color space. - Successfully tracked human w/ various other objects. - Human tracking not enough to track one person, trying combination of HSV tracking - Initial CAD model created w/ Autodesk Fusion 360 - Created PCB for TI chip, looking for fab houses
3	4/12	- Test various motors for behavior/control - Extrapolate movement of object	Completed	- Looking into Stepper vs Servo vs Brushless for optimal control/smoothness - Looking into face tracking as option - Researching how to narrow down human tracking to 1 specific person.
4	4/19	- Sync-up on how to command motors (scaling, etc.) - Create API interface to control motors - Create communication tasks to control motors		- Moving forward with Stepper motor implementation - Using 12V wall adapter as power source, if time permits get a battery sytem - Aim to finish the frame by this weekend, Horizontal part prints on Saturday and Veritical part prints on Sunday - Developing HSV as fallback tracking - Moving forward with Machine learning implementation
5	4/26	- Integration of control system and motor unit
6	5/3	- Control Calibration - Use case test
7	5/10	- Finish Report/Slide deck(?)

Parts List & Cost

ECU:

   RaspBerry Pi 3 Rev B

Brushless Motors:

   x2 4008-70Kv Brushless Gimbal Motor ~$20
       - http://www.hobbyking.com/hobbyking/store/__43041__4008_70Kv_Brushless_Gimbal_Motor_Ideal_for_mid_Style_Cameras_like_the_Nex6_.html

Controllers:

   DRV11873
       - http://www.ti.com/lit/ds/symlink/drv11873.pdf

Phone mount:

   Vince's cheap ass mount ~Free

Whole Enclosure:

   ??? ~???

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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Implementation

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Testing & Technical Challenges

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My Issue #1

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Project Video

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Project Source Code

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References

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References Used

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Appendix

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