P15230 - Quadcopter

System Design ReviewPeter Webster Alyssa ColyettePhilip Tifone James MussiEvan Glenn Andrew Asante

Agenda Overview (Problem Definition)

Project Definition, Problem Statement, Stakeholders, Usage Charts, Customer Requirements, Engineering Requirements, House of Quality, Risk Assessment, Predicted Complications

Functional Decomposition Concept Generation

Competition Course and Rules Outline of Concepts and Attributes Concept Plan A, B

Feasibility Analysis Concept Selection Pugh Analysis (Relevant Datum) Model Justification Backup Plan Project Plan

Project OverviewUnmanned Aerial Systems (UAS) are gaining popularity and demand in thecommercial sector and for personal, hobbyist use. There is an increasingdemand for UAS in areas such as delivery, thermal scanning, and mapping inareas which are time consuming, difficult, or unsafe for a traditionalapproach. For example, Amazon has announced plans for creating a UASdelivery method for their products.

RIT looks to be a pioneer in this field, and has designed a competitionbased around this idea. The competition will require a fully autonomous craftto pilot itself through an obstacle course, using various methods of objectdetection to navigate as well as record data. These crafts will also berequired to carry and drop payloads during the competition, furthering RIT'sresearch into the UAS field.

Problem StatementCurrent State:

Autonomous quadcoptor using AR Drone 2.0 that backs away from obstacles

Desired State:The quadcoptor platform should contain a suite of sensors for object

avoidance and data collection available for use in an indoorenvironment, following the guideline rules for RIT's Imagine RITquadcoptor competition

Goals:Develop platform with commercial, off the shelf (COTS) componentsPlatform has on-board object detection and collision avoidancePlatform is able to recognize faces and record still images.

Constraints:Budget and fundingGovernment defined constraints for hobbyist craftsTechnical Feasibility

StakeholdersStakeholders for Imagine RIT Competition:

- MSD Team- RIT- Competitors- Competition Spectators

Possible Stakeholders:

- Surveyors- Film/Photography- Hobbyists- Public- Objects within Flight Environment

Usage Chart 1

Need to add chart 1

Usage Chart - 2

Need to add chart 2

Customer Requirements

Need to add

Engineering Requirements

Risk Assessment

Need to add risk assessment

Predicted Complications-FAA Regulations - Line of Sight must be maintained

- Flight restrictions on campus due to airport

- Triangulation method requires multiple transmitters

- All calculations must be on-board

- Base must be able to lift equipment and extra payload

- Budget Restrictions

Functional Decomposition

5 Main Categories

Functional Decomp: User Feedback

How

Why

Functional Decomp: Navigation

How

Why

Functional Decomp: Payload

How

Why

Functional Decomp: Imaging

How

Why

Functional Decomp: Safety

How

Why

Concept Generation-Course Layout 5 wireless routers placed around course at known

distances for locational ping mapping 3 main targets at known locations around course Several 8ft tall obstacles placed at unknown locations

around course Course Requirements

Start at one goal (designated) Take photograph of each target’s face Drop hockey puck into finish goal

Concept Generation-Morphological Table

Concept Generation-Benchmark by FunctionA

ir Fr

ames

Sensors

Microprocessor Benchmarking

Concept Generation-Models

Concept Generation-Bench Rigs

Camera

Laser with Line Diffraction

Distances to projected lines are calculated from the camera’s pixel values and projection specs.

Breaks in Line Signify Object Edges

Camera is offset from laser to triangulate distances.

Makeshift Lidar

Concept Generation-Bench Rigs

Real-Time Laser Tracking

Cast:Evan as Evan’s Head

Concept Generation-Failure Modes Sensor Interference (Lighting & fuzzy

obstacles) Propeller Failure (propeller, motor, or control

signal) Current/Power limiter failure (fried

controllers, high powered LASERs) Path Divergence Flawed Wire Connections (OC,SC)

Processing Feasibility AnalysisCan the processor calculate data fast enough to be useful?

Heterogeneous Processing ideal: 80% CPU, 20%

GPU Recommended for

Selection of Processors in Embedded Vision (EVA)

Fit on CPU?

Suitable ASSP?

Implement on GPU,FGPA, or DSP Accelerators

Done

Done

Done

yes

yes

No

No

Processing Feasibility Conclusions choose microprocessor with possible embedded or RT OS chose SoC with opengl support to enable co-processing if needed Olimex products are fully open (hardware and software) personal experience -> lack of documentation with imx233 support community isn’t as lively as RPi or BBB. RPi seems best support, need to check opengl support availability Next Step:

test openCV and Matlab toolboxes on RPI and BBB

Feasibility: PowerMain Governing Equations:

Power = Prop Constant * RPM ^ (Pwr FA)Thrust = pi/4 *D^2pvΔv

Calculated Values Ideal:7”x3” Propeller @ 10,000 RPMProduces 0.69lbs of static thrust40Watts at 100% efficiency

Losses:Brushless Dc Motors ~87%Power Fets On ~1Ω

Additional power circuitry:2 x Raspberry Pi ~20 Watts3 x Servos ~5 Watts1 x Lidar laser~ 0.5 Watt1 x Flight controller ~ 5 WattsTotal 30.5 Watts

Battery Requirements:Flight Time 10 min:3.2Ah BatteryFlight Time 15 min:Scales to 4.5Ah Battery

Feasibility - TriangulationTriangulation is the process of identifying a point in 2D or 3D space bymeasuring distances and angles from two or more fixed, known points in space.

Triangulation will be used as the main method of navigation of the quadcoptor through a 3 dimensional Obstacle course, and is best solved using a combination of prototyping and analysis.

In 2D space, 2 to 4 transmitters can be used to judge the quadcoptor'sposition. In 3D space, height is a necessary factor, and a standard triangle or square setup will not allow for height measurements to be taken. To fix this issue, a transmitter will be placed above the course, allowing for 3D locations to be measured. In total, there will be 5 transmitters - 1 in each corner of the course, and one above the course.

2D Triangulation

Feasibility - TriangulationUsing a method of triangulation called Multilateration (MLAT), the quadcoptor will be able to accurately determine its position in 3D space. Multilateration is a technique in which the quadcoptor will ping each transmitter, and find thetime differences between each. The downside to this method is the initial accuracy. In order for the quadcoptor to obtain an accurate location, multiplemeasurements must be taken and analysed.

All of these calculation must be made on-board, and as such the quadcoptor can not export the calculations to an external computer on site. In order to help with this, an entire processor will be dedicated to only navigation related calculations.

In short, triangulation is completely feasible for use in navigation, but will require a well-built AI algorithm in order to function properly.

3D Triangulation

Feasibility - LiftThe more powerful quadcopters within our scope can lift to 1000 g in addition their base weight. Saving additional weight greatly decreases cost and power requirements.

Most quadcopter systems spec their payload capacity with battery included, but our battery is going to be heavier.

An estimated payload of 510 g, including our battery, makes lift easily feasible for our concept.

Lidar Feasibility

Approach Applying the Radar Equation Object Surface(s)

Other Parameters

Good Engineering JudgmentSurfaces are reflective enough due to the scattering solid angle of object(s)Laser beam will be focus enough to significantly limit refraction and Diffraction

LIDAR LITE HDL- 32E RPLIDARDimension(with Optics)51mm X 30mm 39mm

Dimension5.9”X 3.4”

53mmX31mmX40mm

PerformanceRange LED/Pin:0-5mAcquisition time: 0.05

PerformanceSensor:360 field viewRange:80-100m

Laser wavelength: 785nmRange:0.2-6m

Cost:7999($)

Cost:98.56($)

Cost:398($)

Budget Feasibility

Concept Selections

Concept Selection ModelsConcept 2

Concept 3

Concept 1

Datum: Prev Project

Datum: Concept 1

Datum: Concept 2

Datum: Concept 3

Chosen Model

Model JustificationCritical Criteria Reason

Object Detection Distance LIDAR provides 2 reference points with minimal computational load compared.

Cheap Sensor costs will be reduced with self implemented LIDAR. DIY Base Kit will decrease the costs of quadcoptor base and motors

Public Safety Plastic 3D printed blade shrouds are cheap and should be sufficient in protecting public from spinning propellers

RT Awareness Lessening the processing load for obstacle detection will provide faster inputs to pathing.

Model JustificationPayload Security The claw will hold the payload securely,

and hold it close to the quadcoptor to avoid unwanted collisions

Minimal Sensor Load 3 Sensors - 2 Lidar for forward movement, 1 Sonar for downward movement

Navigation Methods Triangulation is quick and reliable, and has been solved for 3D navigation.

Battery Choice LiPo Batteries have the highest energy density, and have the ability to push the highest current through a circuit.

Backup Plan

Possible Failure

Image Detection

Claw Device

Flight Controller

Triangulation

Anticipated Solution

Make Use of Pixy(CMUcam5)

Suction Cup Design

Choose Alternate Flight Controller

Change to IR Beacon Method

Project Plan

Project Plan Continued

Questions?