Group #2 / Embedded Motion Control [5HC99] Embedded Visual Control 1 Group #5 / Embedded Visual Control Self-Balancing Robot Navigation Paul Padila Vivian

Group #2 / Embedded Motion Control

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[5HC99] Embedded Visual Control

Group #5 / Embedded Visual Control

Self-Balancing

Robot Navigation

Paul Padila

Vivian Zhang

Amritam Das

Michail Papamichail


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Overview

1. Introduction

2. Objectives

3. Design

4. Control

5. Vision

6. Conclusions and Recommendations



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1. Introduction


► The Self-balancing robots not so popular and do not have many applications yet.

► They are mostly used for educative purposes

► Possible reason: hard to be stabilized under certain conditions.

Application of self-balancing robot

Two wheels self-balance electric scooter


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1. Introduction


► The color tracking method is also not very popular yet.

► Possible reasons could be that colors are hard to be tracked during intense sunshine or during the night.

Application of color tracking method

Color-based Object Tracking in Surveillance


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1. Introduction


► The camera is mounted in a robot and not anchored in a wall.

► The robot have automated navigation and can scout areas.

► Limits the amount of cameras that are needed.

► Cameras cannot be tricked by changing clothing color in blind spots.

► There can be a network of cameras that can track the target cooperatively.

Possible application in the future

By Combining the previous two applications one can achieve

a new improved Surveillance system with great advantages.


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1. Introduction


► Gesture detection

► Shape detection

Playstation eye gesture detection

Other visual methods


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1. Introduction


► A robot that can perform certain tasks in a hospital.– Empties the trash.– Refills supplies.

Future applications in general

By Combining a self-balanced robot with any of the visual

method of detection.

► A robot that can identify flawed parts in constructions.– It recognizes skewed shapes.– It can work even if the construction site is closed.– It increases the safety of the construction site.– It protects the investments.


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3. Design


Mechanical Design

► Multi-layer.

► Rigid supports.– Electronics– Motors


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3. Design


Support Structure► The selected thickness of

the material is able to support the weight of the set of batteries used.

► This material is lightweight (minimizes the total weigh). This means an improvement in the energy consumption of the robot.

► MDF is easy to and inexpensive material that can be used with laser cutting machines.


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3. Design


Plates


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3. Design


Motor Base

► Motors should be perfectly aligned.

► Misalignment causes vibrations and deviations during the displacement of the Robot


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3. Design


Motor

► Functions:– Stabilization – Displacement of

the robot

►Fast reactions

►Large torque


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3. Design


Batteries

► Maximum energy consumption: 12V at 5.2A.

► 18650 batteries: 3.7V(x3) at 5.3A


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3. Design


Arduino Shield

► Compact and easy to install.

► The interfaces between the sensors and the control are ready to use

►MPU-6050: 3-axis gyroscope and a 3-axis accelerometer in a single chip with I2C communication

►L298P: Motor driver, high voltage (50V) and high current (4A) dual channel full-bridge


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3. Design


Arduino UNO

► Control unit.– Sensors– Actuators


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4. Control


Control Problem

► Stabilization Problem

► Position Problem


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4. Control


Stabilization Problem

► P

► PD

► PI


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4. Control


Position Problem

► P

► PD

► PI


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4. Control


Control Design

► Different control objectives.

► Same actuator.

► Different time constants are fundamental to guarantee stability


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4. Control


Performance

Parameter Value

Settling time 3 sec

Position tolerance

+/- 4cm

Tracking tolerance

+/- 4 cm


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Vision

–Image Processing– Colour Tracking

– Open CV

– Integration with Control

– Hardware

– Object Following


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Choice of Hardware - Raspberry Pi 2 + pi Camera


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Capturing Consistent Image

► To fix exposure time, set the shutter_speed attribute to a reasonable value.

► To fix exposure gains, let analog_gain and digital_gain settle on reasonable values, then set exposure_mode to 'off'.

► To fix white balance, set the awb_mode to 'off', then set awb_gains to a (red, blue) tuple of gains. Optionally, set iso to a fixed value.

http://picamera.readthedocs.org/en/release-1.9/api.html#picamera.PiCamera.shutter_speed

http://picamera.readthedocs.org/en/release-1.9/api.html#picamera.PiCamera.analog_gain

http://picamera.readthedocs.org/en/release-1.9/api.html#picamera.PiCamera.digital_gain

http://picamera.readthedocs.org/en/release-1.9/api.html#picamera.PiCamera.exposure_mode

http://picamera.readthedocs.org/en/release-1.9/api.html#picamera.PiCamera.awb_mode

http://picamera.readthedocs.org/en/release-1.9/api.html#picamera.PiCamera.awb_gains

http://picamera.readthedocs.org/en/release-1.9/api.html#picamera.PiCamera.iso


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Capturing Consistent Image – Sample Implementation


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Color Tracking

► Image Conversion


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Noise Elimination

► Morphological Operation

► Erosion

Dilation


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Thresholded Image with Morphological Operation


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Edge Detection + Contour Analysis

► Canny Edge Detection + Gaussian Blur Filter


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Integrating Raspberry pi with Arduino

► Serial Communication


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Object Tracking Algorithm

► Boolean Logic


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Object Tracking Algorithm

► Proportionate Controller


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Performance of Object Tracking

Camera Reaction Time

– camera

– object


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Change in The object Distance


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Movement of the Camera

Documents

Group #2 / Embedded Motion Control [5HC99] Embedded Visual Control 1 Group #5 / Embedded Visual Control Self-Balancing Robot Navigation Paul Padila Vivian