P Quadcopter. System Design Review

Transcription

1 P Quadcopter System Design Review Peter Webster Philip Tifone Evan Glenn Alyssa Colyette James Mussi Andrew Asante

2 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

3 Project Overview Unmanned Aerial Systems (UAS) are gaining popularity and demand in the commercial sector and for personal, hobbyist use. There is an increasing demand for UAS in areas such as delivery, thermal scanning, and mapping in areas which are time consuming, difficult, or unsafe for a traditional approach. For example, Amazon has announced plans for creating a UAS delivery method for their products. RIT looks to be a pioneer in this field, and has designed a competition based around this idea. The competition will require a fully autonomous craft to pilot itself through an obstacle course, using various methods of object detection to navigate as well as record data. These crafts will also be required to carry and drop payloads during the competition, furthering RIT's research into the UAS field.

4 Problem Statement Current 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 indoor environment, following the guideline rules for RIT's Imagine RIT quadcoptor competition Goals: Develop platform with commercial, off the shelf (COTS) components Platform has on-board object detection and collision avoidance Platform is able to recognize faces and record still images. Constraints: Budget and funding Government defined constraints for hobbyist crafts Technical Feasibility

5 Stakeholders Stakeholders for Imagine RIT Competition: - MSD Team - RIT - Competitors - Competition Spectators Possible Stakeholders: - Surveyors - Film/Photography - Hobbyists - Public - Objects within Flight Environment

6 Usage Chart 1 Need to add chart 1

7 Usage Chart - 2 Need to add chart 2

8 Customer Requirements Need to add

9 Engineering Requirements

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11 Risk Assessment Need to add risk assessment

12 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

13 Functional Decomposition 5 Main Categories

14 Functional Decomp: User Feedback Why How

15 Functional Decomp: Navigation Why How

16 Functional Decomp: Payload Why How

17 Functional Decomp: Imaging Why How

18 Functional Decomp: Safety Why How

19 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

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21 Concept Generation-Morphological Table

22 Concept Generation-Benchmark by Function Air Frames Sensors

23 Microprocessor Benchmarking

24 Concept Generation-Models

25 Concept Generation-Bench Rigs Makeshift Lidar Distances to projected lines are calculated from the camera s pixel values and projection specs. Breaks in Line Signify Object Edges Camera Laser with Line Diffraction Camera is offset from laser to triangulate distances.

26 Concept Generation-Bench Rigs Real-Time Laser Tracking Cast: Evan as Evan s Head

27 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)

28 Processing Feasibility Analysis Can 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? yes Done No Suitable ASSP? yes Done No Implement on GPU,FGPA, or DSP Accelerators Done

29 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

30 Feasibility: Power Main Governing Equations: Additional power circuitry: Power = Prop Constant * RPM ^ (Pwr FA) Thrust = pi/4 *D^2pvΔv Calculated Values Ideal: 7 x3 10,000 RPM Produces 0.69lbs of static thrust 40Watts at 100% efficiency Losses: Brushless Dc Motors ~87% Power Fets On ~1Ω 2 x Raspberry Pi ~20 Watts 3 x Servos ~5 Watts 1 x Lidar laser~ 0.5 Watt 1 x Flight controller ~ 5 Watts Total 30.5 Watts Battery Requirements: Flight Time 10 min: 3.2Ah Battery Flight Time 15 min: Scales to 4.5Ah Battery

31 Feasibility - Triangulation Triangulation is the process of identifying a point in 2D or 3D space by measuring 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's position. 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

32 Feasibility - Triangulation Using 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 the time differences between each. The downside to this method is the initial accuracy. In order for the quadcoptor to obtain an accurate location, multiple measurements must be taken and analysed. All of these calculation must be made on-board, 3D Triangulation 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.

33 Feasibility - Lift The 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.

34 Lidar Feasibility Approach Applying the Radar Equation Object Surface(s)

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36 Other Parameters

37 Good Engineering Judgment Surfaces 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 RPLIDAR Dimension (with Optics) 51mm X 30mm 39mm Dimension 5.9 X mmX31mmX40mm Performance Range LED/Pin:0-5m Acquisition time: 0.05 Performance Sensor: 360 field view Range: m Laser wavelength: 785nm Range: 0.2-6m Cost: 7999($) Cost: 98.56($) Cost: 398($)

38 Budget Feasibility

39 Concept Selections

40 Concept Selection Models Concept 2 Concept 3 Concept 1

41 Datum: Prev Project

42 Datum: Concept 1

43 Datum: Concept 2

44 Datum: Concept 3

45 Chosen Model

46 Model Justification Critical 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.

47 Model Justification Payload 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.

48 Backup Plan Possible Failure Anticipated Solution Image Detection Make Use of Pixy(CMUcam5) Claw Device Suction Cup Design Flight Controller Choose Alternate Flight Controller Triangulation Change to IR Beacon Method

49 Project Plan

50 Project Plan Continued

51 Questions?