Preliminary Design Review: Lab Report

Submitted to: Dr. Bixler

GTA Omar El-Khoury

Created by: Team B

Josh Burton

Brook Cannon Vivian PAng

Kyle Slavinski

Engineering 1182

The Ohio State University Columbus, OH

1 November, 2016

Executive Summary The galactic empire hired a group of engineers to develop an advanced energy vehicle (AEV) that will be used to transport R2D2 units from their assembly station to their final destination. The AEV utilized an advanced networking system, akin to a monorail, as a logistical aid. The planet on which the AEV functions is remote and limited in power supply. Thus, the engineers were to develop the AEV so that energy consumption was efficiently managed. Additionally, limiting total expenses accumulated during development was of utmost importance. The AEV had to also meet certain design constraints in both its software and its physical construction. Due to the remote planet’s shifting faults, the monorail system may accumulate perturbations. Therefore, the AEV was required to maintain operational consistency despite any changes that occurred on the track. The engineers were required to create a scaled model of such an AEV that met the operational requirements, design constraints, and minimized the energy/mass ratio. The AEV was developed over a series of 8 labs, wherein each lab focused on one of the previously mentioned characteristics. During the first lab, the Team became familiar with the code and Arduino circuitry. The team also qualitatively analyzed the response of the electric motors to Arduino function calls. The second lab period involved testing the most efficient thruster format as well as the sensors involved in determining the AEV’s position. Through the use of wind tunnel testing, the team discovered that the ‘pusher’ propeller format, where the thrustline is opposite the face of the propeller, allowed for the greatest power output compared to the ‘puller’ propeller format, the opposite of pusher format. Specifically, the ‘pusher’ format exhibited a power efficiency of 60% with an advance ratio of 1.5 while the ‘puller’ format exhibited less than 2% at a similar advance ratio. During lab 3, the team gathered data, such as time or voltage, from an AEV run. This data was analyzed using a MATLAB program to calculate energy and power vs time and distance plots. From these plots, it was discovered that certain phases of the AEV’s run used varying amounts of power. For instance, while the AEV accelerated, the AEV exhibited an increase in power consumption. These data will be featured in the results section. Following the gathering of the technical data, the team designed and compared different AEV concept designs. In total, four designs were proposed, each with a different, but equally important, physical attributes. In order to quantitatively compare these designs, the team first conducted a screening analysis, where the designs were individually compared to a reference design as an experimental control. The team subsequently scored each design by placing numerical weights on qualities of various importance that would affect the performance, such as center of gravity or cost. After conducting this analysis, the team determined the design they would be using for performance testing. This design was had a stable center of balance and was low in cost, weight, and design complexity, which made it adaptable to any changing needs. The last task the team performed was a performance test of the proposed AEV design. In addition to this initial design, the team also developed a second design that exhibited different traits from the first. The second design mainly featured a ‘pusher’ format for both forwards and backwards directions, which would hypothetically be more power efficient than the first. Both designs are featured in the appendix. From the data the team gathered, the team suggests that a ‘pusher’ propeller format be used in the AEV. Additionally, by studying the different phases of the AEV runtime, the team suggests that minimizing the accelerating periods in the code would maximize energy efficiency. Additionally, the

1

team suggests that concept design two be used for further testing. The results indicated that it consumed less power over the span of its run. Additionally, its design simplicity offers adaptability and cuts costs in materials.

Table of Contents

Introduction……………………………………………………………………………………………………………………….

3

Experimental Methodology………………………………………………………………………………………………..

3

Results……………………………………………………………………………………………………………………………….

5

Discussion………………………………………………………………………………………………………………………….

9

Conclusion and Recommendations…………………………………………………………………………………….

10

Appendix (Supplemental Info, Long Term Schedule, Solidworks Models)....……….……………...

13

2

Introduction The team was tasked with designing an alternative energy vehicle (AEV). The AEV design was required to meet operational requirements, design constraints, and have a minimized the energy/mass ratio. While keeping these constraints in mind, the team had to design an AEV that was capable of traversing a monorail track and reaching a cargo loading station. From here the AEV needed to return to the starting point with cargo in tow. Operationally, the AEV needed to complete this task efficiently, and needed to be programmed so as to properly function under any possible inconsistencies in the cargo or track. Due to low energy availability, the AEV had to be energy efficient. The purpose of this project was to not only teach technical skills but to also instill concepts of project planning, design creation, and teamwork into students. The labs preceding the performance tests for the AEV project were designed to introduce students to the basic concepts underlying the development of the AEV. Throughout the beginning labs, the team became familiar with the coding syntax of the Arduino. Additionally, the team became familiar with the hardware components that work concomitantly with the Arduino code, thereby allowing the AEV to function autonomously. The subsequent labs were performed with the goals of increasing familiarity in system and data analysis. Analyses were performed on both propeller formats and AEV runtime via a wind tunnel and a matlab toolset respectively. The purposes of these analyses were to gather information on the most optimal performance of the AEV. By performing these tests, the team could better grasp at the best approach to developing an AEV that met design requirements, as outlined by the MCR. With the aforementioned concepts in mind, team members individually designed different AEV prototypes that sought to optimally perform the task at hand. The creation of individual AEV designs had the added benefit of offering intrinsic brainstorming to the design process. By having this brainstorming session, the team was able to create a design that would function in an increasingly efficient manner. The team chose the best design from this brainstorming session and used it in future tests. After the introductory labs were performed, the team designed an additional prototype. This design was subsequently compared to the previous design, as determined by design screening and scoring. In addition to the screening and scoring methods used in the past, the two AEV prototypes were compared using a set of code created by the team and analyzing the energy efficiency. By doing so, the team was able to narrow in on the most energy efficient design that met the required design characteristics. The information discussed herein contains the results from previous laboratory periods as well as results from the first week of performance testing. The results include, system analyses, screening and scoring matrices, Arduino code, and AEV prototype drawings and models. Additionally, the problems

3

that arose from both technical difficulties and team interactions will be discussed at length. Overall, this report summarizes all that was previously achieved throughout the past weeks of the AEV design process.

Experimental Methodology The labs that were conducted prior to the the first performance test allowed the team to gain the necessary background skills that were needed to design an AEV. The first three labs largely focused on data analysis of AEV components, including both hardware and software factors. During the first laboratory period, students were to run functional commands through the Arduino circuitry to a pair of electric motors. This was accomplished by connecting the motors to the proper Arduino outlets. The pictorial below shows the layout of the Arduino and the location of said outlets. A program using AEV-specific function calls was written in the Arduino IDE. This program utilized several basic commands to control the electric motors, such as increasing or decreasing the power output to the electric motors.

Arduino Circuitry with Electric Motor Inputs

The subsequent laboratory period involved testing and analyzing two hardware components, the reflectance sensors and the propellers. The reflectance sensors utilized small phototransistors and infrared LEDs to measure the reflectance of a surface. In this case the surface was reflective tape located on the surface of the wheels for the AEV. These sensors were attached to the side of the wheel containing the reflective tape, so as to measure number of wheel revolutions as the wheel rotated. The information gathered from the reflectance sensor was sent to the Arduino, which was actively used monitor position of the AEV. The propellers of the AEV were also tested. This experiment involved the utilization of a wind tunnel to test for the propulsion efficiency of different propeller configurations − specifically pusher and puller formats. A single AEV propeller and electric motor were placed in a thrust stand inside the wind tunnel. Varying levels of voltage and current were supplied to the motors and the available thrust was measured accordingly. The power output and propulsion efficiency was then calculated using equations 1 and 2 respectively.

Power output in terms of Propeller Advance Ratio

ropeller Advance Ratio (J) elocity(v)/ [(RPM/60) iameter(d)] 00P * v *D * 1

(1)

Propulsion Efficiency

ropulsion Efficiency (%) Power out (W ) / Power in (W ) 100P = * (2)

4

Lab 3 continued with data analytics. The team built a reference AEV and loaded a short program onto the Arduino. Following the completion of the AEV run using the aforementioned code, the team gathered performance information that was stored on the Arduino. This performance information included time, distance, position, voltage, and current. These data were recorded in EEPROM units. Using a MATLAB program designed by the team, the EEPROM units were converted to physical parameters (i.e. volts or amperes) to be used in further calculations for energy and power. The team then utilized MATLAB data analysis GUI to create plots of power vs time and distance. The final labs involved becoming familiar with overall AEV design processes. The team individually built and designed four AEV prototype concepts. The team compiled a list of success criteria that could possibly contribute to the overall performance and cost of the design. These success criteria were utilized in decision matrices to draw comparisons between each design. As a control, the designs were compared to a pre tested reference AEV. Two types of decision matrices were constructed − a concept screening matrix and a concept scoring matrix. The former was used to draw quick comparisons between each design. In particular, each design was given a zero, plus, or minus symbol corresponding to each success criteria. The zeros corresponded to neutral benefit, while the plus and minus symbols represented positive and negative benefit as they related to a specific success criterion. The design with the most positives and least negatives moved on for further testing and consideration. Concept scoring matrices offered a more quantitative approach to the comparing process. In this case, each success criteria was given a numerical weight corresponding to its overall effect on the design performance. Like before, each design was ranked. In this case, the designs were ranked on a scale of 0-5 points. The total score for each design was calculated by multiplying the rating by the weight and taking the sum of these values for each design. The design with the greatest score moved on for further testing. After the introductory labs were completed, the team moved on to performance tests. During the first test, the team designed an additional AEV prototype to be tested alongside the chosen AEV prototype. The designs were tested using an Arduino program. The prototypes were compared using decision matrices. The energy efficiency was also taken into account when choosing the best design. The best design was chosen in accordance with the relative scores in the decision matrices as well as considering energy efficiency.

Results The first lab was meant to familiarize the group with the basics of programming and configuring the AEV. For the most part, that goal was accomplished. However, technical difficulties such as arduino wiring, and trouble with the group computers delayed the lab, and therefore the goals laid out in the lab manual were not all met. Lab 2 introduced external sensors and how they could be used to program the AEV in different ways. Coding using the reflectance sensor on the AEV allowed programming based on distance traveled. The second lab had a second component where a wind tunnel was used to determine propulsion efficiency of the AEV’s propellers. The specific configuration group B tested was the 3030 puller configuration which resulted in an unexpected linear scaling of advance ratio with propulsion efficiency. An aggregation of the larger sample data for all configurations was collected from the class and it was determined that a pusher configuration favors a higher advance ratio and a puller is more

5

efficient at lower advance ratios. The class data for 2510 pusher and puller are shown in figures 1 and 2

Figure 1: 2510 Propeller in a Puller Configuration. Propulsion

Efficiency as a Function of Advance Ratio

Figure 2: 2510 Propeller in a Pusher Configuration. Propulsion

Efficiency as a Function of Advance Ratio

Lab 3 focused on using a design analysis tool in order to download data collected by the AEV about power usage. Figure 3 depicts a plot of power output with respect to time. This figure shows that constant power usage tends to correlate with constant motor power settings, with spikes and steep slopes for motor acceleration. Also it was shown that higher power settings resulted in higher power usage. Table 1 shows this information in a tabulated format.

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Figure 3: Power as a Function of Time

Table 1: Phase Breakdown with Corresponding Code and Energy

Phase Arduino Code Relation Time interval (sec)

Total Energy (Joules)

Phase 1 Accelerating 0.06-2.04 12.0100

Phase 2 Constant speed 2.04-8.58 29.3200

Phase 3 Accelerating 8.58-8.94 3.0191

Phase 4 Deccelerating 8.94-10.2 9.9826

Phase 5 Constant Speed 10.2-13.08 0.0735

Total Energy Used:

54.4051

The fourth lab was related to AEV creative design. Each team member drew their own version of an AEV design and were to take note of specific obstacles of creative design. The designs were all discussed and a preferred one was chosen to move forward with. This design was expanded upon in future labs. Lab 5 consisted of using data matrices to provide a quantitative comparison between the design made in the previous lab. Tables 1A and 2A (Appendix) show the comparison of individual designs against the stock AEV design. Kyle’s design was the best so it was chosen to move forward with. This brings us to the first performance test, where Kyle’s design and one other were created and tested using portions of a rough draft of the final code. Figures 6 (4) and 7 (5) depict the concept designs for each prototype and figures 8(6) and 9(7) will show the performance comparison in terms

7

of power output over a time span. For both concept designs, tables 2 and 3 break down the power consumption in terms of the functions that relate to the corresponding AEV behavior.

Figure 6: Isometric View of Concept 1 Figure 8: Phase Diagram of Concept 1 Run

Table 2: Phase Breakdown of Concept 1 Run with Corresponding Code

Phase Arduino Code Relation Time interval (sec)

Phase 1 Accelerating 0-1

Phase 2 Constant speed 1-13

Phase 3 deccelerate 13-13 (instant)

Phase 4 Brake (drift) 13-30

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Figure 7: Isometric View of Concept 2 Figure 9: Phase Diagram of Concept 2 Run

Table 3: Phase Breakdown of Concept 2 Run with Corresponding Code

Phase Arduino Code Relation Time interval (sec)

Phase 1 Accelerating 0-1

Phase 2 Constant speed 1-11

Phase 3 deccelerate 11-11 (instant)

Phase 4 Constant speed 11-21

Phase 4 Brake (drift) 21-40

From figures 6 and 7, concept design 2 utilizes less power over the same span of time. This shows the

second concept is more energy efficient. The group expected this result, as design 2 seemingly had

little air resistance. Design 2 was also lighter than design 1 (refer to drawing packets), which would

allow the AEV to move with less power. Additionally, based on the concept screening and scoring

matrices (Tables 3A and 4A in Appendix) design 2 achieved the highest rating for the chosen success

criteria. Therefore, concept design 2 was chosen to be used in further performance tests.

Discussion Lab 1 introduced the basics of Arduino programming and provided a foundation for all of the following labs. It also provided a series of challenges, which came in the form of technical difficulties such as arduino wiring and computer troubleshooting. Overcoming these obstacles taught invaluable lessons as the team knew exactly what to do when these problems inevitably arose again in later stages. Potential error arose when the code was incorrect or something was incorrectly assembled, but there were no measurements in particular that contained inherent error. Lab 2 built on Lab 1, with the external sensors and further programming introduction. It also showed how the propellers and motors behaved in different configurations. Group B tested the 3030 puller configuration and the data showed that efficiency went up linearly with advance ratio, which according to theory should not be the case. The source of this error is currently impossible to determine, as the handling of the wind tunnel equipment was not controlled by team members.

During lab 3, the group began to fall behind. Several Technical difficulties were encountered. The arduino needed to be rewired, and codes and programs were not working as expected. Fixing everything that went wrong in lab 3 was critical as these issues arose later and were corrected quickly from what was learned. After the technical difficulties were mediated, data was acquired. Eventually, it could be determined from the collected data that power consumption was linearly related to power

9

setting and remains constant at constant velocities. It also showed steep slopes for acceleration. This all was expected and matched theory.

After the team individually created potential AEV designs, Kyle’s design proved the most adaptable and most practical; it was the favored choice moving forward. The other designs, such as Brook’s, were too heavy and impractical. Aesthetics took a backseat to practicality in this design process. Using materials already in the AEV kit would be ease the development process compared to acquiring new materials and adapting it to the existing AEV components.

Lab 5 quantified this decision with a decision matrix that directly compared the individual designs to the stock AEV. This provided the group with a method to evaluate concept designs as the AEV development process progressed. Kyle’s design featured a modified version of the stock AEV, with a more streamlined configuration and some parts removed for savings in weight.

The information acquired in all previous labs was utilized in testing in Performance Test 1. Unfortunately, the battery caught fire during testing which delayed this phase of the lab. Once everything was reconstructed and repaired, the AEV, concept design 1, was tested on the track with a portion of a rough draft of the final code. Power usage data was collected, and the code was fine tuned. A second concept for the AEV was constructed, and tested with the exact same code for concept 1. This concept moved the motors to either end of the AEV, resulting in a pusher/puller configuration for any phase of the code. This also allowed single motors to be used while keeping the thrust in line with the center of gravity of the AEV. While power vs time plots were able to be constructed, due to time constraints specific energy usage information was unable to be obtained. This is largely due to the fire that occurred early in the testing process. Overall, concept 2 was significantly more efficient than concept 1, so it was chosen to used in further testing. Images of the AEVs and their respective drawings are located in the results section for reference.

Conclusion and Recommendation Lab 1 was an introductory to the course syllabus and served to familiarize students with basic arduino programming. During the lab the team set up sketch book and practiced how to write and download code, as well as test the code using the Arduino. After completion of the lab, an understanding of basic arduino function calls, uploading codes to the arduino, and troubleshooting techniques was obtained. The incompletion of scenario one was due to several failures of equipment. A faulty computer connection was found which led to the inability to load code to the arduino. This was solved by simply changing to a different computer. Once the code was successfully uploaded the propellers only achieved small twitching motions, never being able to accelerate to 15 percent power. This was recognized as a failure of the power source. It is very important that all equipment be properly tested for function, and all instructions are carefully followed prior to working toward completing lab objectives.

During lab 2 the tests regarding the reflective sensor test was successful and allowed further

programming of the AEV. While testing on the outer track, the AEV was programmed to travel 16 feet,

stop, and reverse for a brief time. The team was unable to complete the task at hand and additional

time was needed outside of class to get the AEV to behave exactly as programmed and expected . For

the wind tunnel, a 3030 puller configuration was used. The thrust in grams scaled nearly linearly with

10

the power setting, which also leads to an increase in propulsion efficiency as the advanced ratio

increased. From the results and data that were acquired from the wind tunnel test, it was evident that

the pusher configuration had the greatest propulsion efficiency while the puller configuration showed

the greatest efficiency for lower advance ratios. Overall, the takeaways from lab 2 were the ability to

utilize the reflectance sensors in order to upload codes to the AEV that measure and control the

distance the AEV is traveling. The data from the wind tunnel indicated that a push configuration will

be the most efficient for future AEV designs due to the higher propulsion efficiency for a given

advance ratio. The team recommends that tests be performed on the reflectance sensors before

beginning to program in order to make sure that the AEV is calculating distance correctly. If this is not

done, the AEV could miscalculate distance or it could count negative distance as positive and vice

versa.

During lab 3 The team monitored the five phases of power which were corresponded to the runtime

of the AEV. The results indicated that each phase correspond to different function calls, which are the

accelerating phase, constant speed phase, acceleration phase, deceleration phase, and the constant

speed phase . After analysing the results, the data indicated a constant power usage during times of

constant speed (phases 2 and 5), with surges and peaks during times of acceleration (phases 1 and 3).

This data will be used to program the most energy efficient profile for the AEV, taking into account

which phases costed more energy, and which used the least. This will help determine the optimum

code for energy conservation. The team learned that data can be downloaded from the AEV for

further analysis. Additionally, raw data can be used to derive further data consisting of physical

parameters. Via the use of the MATLAB analysis tool, the calculation can be done immediately.

Analyzing the the power vs time graph allowed for further improvements of the design. It was

determined from the data that Acceleration requires more power than remaining at a constant speed.

The team recommends that the final AEV code utilizes fewer acceleration phases alongside increased

drifting phases in which the engines to do not use power. By doing so, the total energy used by the

AEV would be cut significantly. Additional analysis of the data will be used later in the project to make

our code as efficient as possible.

The subsequent lab involved each member to designing a prototype design for the team to decide

upon. After much consideration and discussion among the members, Kyle’s design was chosen due to

it’s simplicity and adaptability . Throughout this lab, the team agreed upon the important criteria that

needs to be implemented in the AEV design, the weight, aerodynamic body, and energy conservation

of the AEV was deemed to be most important. During the process of this lab, the team learned

brainstorming is key to creating innovative designs that would help to complete the task at hand.

Also, an unbiased approach must be used when making decisions as a group. The team must consider

the pros and cons respectively, this allows the team to decide upon the best design. It was also crucial

to take into consideration how the AEV would interact with its environment (i.e. the track). It was

imperative that the design was kept as simple and adaptable as possible. A simple design will allow

the vehicle to be robust and easy to maintain. A simple design would also allow for more time spend

on performance testing in the future. An Adaptable design would allow for more opportunities to

mold to different situations as needed.

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The forming of concept screening and scoring matrix in Lab 5 assisted tremendously in the decision making process of the group. After creating a list of success criteria of the AEV, each criteria was applied a numerical weightage of importance. The designs were ranked by their total score, which made the decision making process more objective and pragmatic . Kyle’s design was a clear cut winner in terms of overall screening and scoring. The design used fewer parts, which contributed to a decrease cost. It’s lightweight body contributed to the winning aspect of the design, where most of the weight came from the battery and arduino board. It was difficult to improve upon the design in that area. Additionally, the body of the AEV was aerodynamic promoted adaptability and any form of changes and improvements. The most important ideas to be taken from this lab are a data matrix could be used to directly compare designs. The matrix and table are crucial in showing how the various aspects of scoring measure up to each other across all designs simultaneously. A recommendation for the future would be a concept screening and scoring matrix should be used for any major decision that is required to be made. This will promote making important decisions based on a collaborative team effort and scientific reasoning. Lab 8 or Performance Test 1 is separated into 3 different parts throughout the week .Throughout lab 8 , the group made sure that we improved the designs and tested both designs to compare with each other. While conducting a pre-run , the lithium battery caught fire .After overcoming all the difficulties , the testing process was very smooth . During the test of the first design was unable to achieve forward motion. It was determined that a plastic lip at the beginning of the track was impeding the motion of the AEV, so extra power was added to the code in order to solve this problem. After the code was adjusted both designs were able to achieve forward motion and complete their code. The team learned that the second design was superior to the original design .It was discovered that the second design is lighter and more adaptable than the initial design . The position of both propellers in opposite direction of each other allowed the AEV to implement pusher configurations for both directions of movement. This configuration provided more thrusts and power to the AEV as compared to the installation of propellers on the sides of the AEV Lab 9 or Performance Test 2 is again separated into 3 parts throughout the week At the conclusion of testing, it was determined that the the power to energy ratio needed by the second AEV is lower. The second AEV design is the clear winner. Recommendations for the future of this design are to utilize the new capabilities of the very different propeller configuration to the team’s advantage. This propeller configuration is not only more efficient, but it also allowed the AEV itself to be more maneuverable, as the AEV could now be controlled by utilizing propellers from opposing directions.

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Appendix

Table 1A: Concept Screening Matrix for Initial Individual Designs

Table 2A: Concept Scoring Matrix for Initial Individual Designs

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Table 3A: Concept Screening Matrix for Two Final Prototype AEV Designs

Table 4A: Concept Screening Matrix for Two Final Prototype AEV Designs

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Lab 1 Schedule

No. Task

Star

t Finish

Due

Date

Est

Time Kyle

Broo

k

Josh Vivian

%

Complet

e

1.

Setup the AEV

Software

30-

Aug

30-

Aug

6 -

Sept 1.20 0.20 0.20 0.20 0.20 100%

2.

Program the

basic function

calls controlling

the AEV

30-

Aug

30-

Aug

6 –

Sept 0.20 0.10 0.10 100%

3.

Upload

Programs on

the Arduino

30

-Aug 30-

Aug

6 –

Sept 0.20 0.10 0.10 100%

4.

Learn

troubleshootin

g techniques

30-

Aug

30-

Aug

6 -

Sept 0.20 equal equal equal equal 100%

Lab 2 Schedule

No

. Task

Star

t

Finis

h

Due

Date

Est

Time Kyle Brook

Josh Vivian

%

Compl

ete

1.

Setup Reflectance

Sensor

6-Se

pt

6-Sep

t

13-

Sept 1.20 0.20 0.20

0.20

0.20 100%

2.

Load Sensor Function

Calls

6-Se

pt

6-Sep

t

13-

Sept 0.20

0.10

0.10 100%

3.

Troubleshoot

sensors

6-Se

pt

13-Se

pt

13-

Sept 0.20 0.10 0.10 100%

4.

Set up propulsion

system

6-Se

pt

6-Sep

t

13-

Sept 0.20 0.10

0.10

100%

5.

Test propulsion

system

6-Se

pt

6-Sep

t

13-

Sept 0.20 0.10 010 100%

15

6.

Link wind tunnel to

AEV

6-Se

pt

6-Sep

t

13-

Sept 0.20 0.10 0.10 100%

Lab 3 Schedule

No. Task Start Finish Due

Date

Est

Tim

e

Kyle Brook Josh Vivian %

Complete

Part A : System Analysis 2 : Performance Data Analysis

1. Downloading

EEPROM data

13 -

Sept

13 -

Sept

27

-Sept

1 1 100%

2. Convert

EEPROM data

to physical

parameter

13 -

Sept

13 -

Sept

27 -

Sept

2 1 1 100%

3. Calculate

performance

characteristic

using physical

parameter

13 -

Sept

13 -

Sept

27 -

Sept

1 1 100%

Part B : Design Analysis Tool

4. Upload wind

tunnel data

into design

analysis tool

13 -

Sept

13 –

Sept

27 -

Sept

2 1 1 100%

5. Upload

Arduino data

into design

analysis tool

13 –

Sept

13 -

Sept

27 –

Sept

3 1 1 1 100%

6. Conduct

performance

analysis of an

AEV operation

13 -

Sept

13 –

Sept

27 –

Sept

3 1 1 1 100%

16

7. Complete

progress

report

13 -

Sept

27 -

Sept

27 -

Sept

6.5 2 1.5 1.5 1.5 100%

Lab 4 Schedule

No. Task Start Finish Due

Date

Est

Time

Kyle Brook Josh Vivian %

Complete

1. Brainstorming

idea

individually

27 -

Sept

27-

Sept

4-

Oct

1.20 0.20 0.20 0.20 0.20 100%

2. Brainstorming

idea as a team

27-

Sept

27-

sept

4-

Oct

0.20 Equal

(in

class)

Equal

(in

class)

Equal

(in

class)

Equal

(in

class)

100%

3. Working on

construction

of the initial

AEV concept

design

27-

Sept

4 -Oct 4-

Oct

4 1 1 1 1 100%

Lab 5 Schedule

No. Task Start Finish Due

Date

Est

Time

Kyle Brook Josh Vivian %

Complete

Technique for design decision making

1. Formulate

Concept

Screening

Spreadsheet

4-

Oct

4- Oct 11-

Oct

2 1 1 100%

2. Formulate

Concept

Scoring

Matrix

Spreadsheet

4-

Oct

4- Oct 11-

Oct

2 1 1 100%

Testing the AEV

17

3. Program the

assembled

AEV

4-

Oct

4- Oct 11-

Oct

2 0.5 0.5 0.5 0.5 100%

4. Test the AEV

on track

4-Oc

t

4- Oct 11-

Oct

1.5 Equal

(in

class)

Equal

(in

class)

Equal

(in

class)

Equal

(in

class)

100%

5. Complete

Progress

Report

4-

Oct

11-

Oct

11-

Oct

4 1 1 1 1 100%+

Lab 7 Schedule

No. Task Start Finish Due

Date

Est

Time

Kyle Brook Josh Vivian %

Complete

1. Complete

Lab

Proficiency

Test

11-O

ct

11-

Oct

11-

Oct

1.20 Equal Equal Equal Equal 100%

2. Discuss Oral

Presentation

11-

Oct

11-

Oct

18-

Oct

1.00 Equal Equal Equal Equal 100%

3. Re-group

and ask

questions

11-

Oct

11

-Oct

18-

Oct

1 Equal Equal Equal Equal 100%

Lab 8 Schedule (Performance Test 1)

Task Start Finish Due

Date

Est

Time

Kyle Brook Josh Vivian %

Complete

Designing : 1st

Design

25-

Oct

25-

Oct

31-

Oct

1.20 0.20 0.20 0.20 0.20 100%

Designing : 2nd

Design

25-

Oct

25-

Oct

31-

Oct

1.20 0.20 0.20 0.20 0.20 100%

Test Run 26- 26- 31- 2 0.30 0.30 0.30 0.30 100%

18

Oct Oct Oct

Analyze Design

and Decision

Making

31-

Oct

31-

Oct

31

-Oct

2 0.30 0.30 0.30 0.30 100%

Lab 9 Schedule ( Performance Test 2)

Task Start Finish Due

Date

Est

Time

Kyle Brook Josh Vivian %

Complete

Arduino Codes 1

-Nov

1 -

Nov

8 - Oct

0.30 0.15 0.15 100%

Test : Speed 1 –

Nov

1- Nov 8 -

Nov

1.20 0.20 0.20 0.20 0.20 100%

Test : Balance

during turns

2 -

Nov

2 -

Nov

8 –

Nov

0.30 0.15 0.15 100%

Test : Brakes 2 -

Nov

2 -

Nov

8 –

Nov

0.30 0.15 0.15 100%

Test : Picking

up and

Delivering

7 -

Oct

7 -

Oct

8 –

Oct

0.30 equal equal equal equal 100%

Discussion :

Test Result

7

-Nov

7-

Nov

8 –

Nov

0.30 0.30 0.30 0.30 0.30 100%

Lab 10 Schedule ( Performance Test 3)

Task Start Finish Due Est Kyle Brook Josh Vivian %

19

Date Time Complete

Test 1 :

Track Run

15 -

Nov

15-

Nov

22-

Nov

1.00 0.15 0.15 0.15 0.15

Test 2:

Energy

efficient

16-

Nov

16-

Nov

22-

Nov

1.20 0.20 0.20 0.20 0.20

Test 3:

Consistency

16-

Nov

16-

Nov

22-

Nov

1 0.15 0.15 0.15 0.15

Test 4:

Complete

track

17-

Nov

17-

Nov

22-

Nov

1 equal equal equal equal

Lab 11 Schedule (Performance Test 4)

Task Start Finish Due

Date

Est

Time

Kyle Brook Josh Vivian %

Complete

Test Run 1

21 -

Nov

21-

Nov

28-

Nov

1.00 0.15 0.15 0.15 0.15

Test Run 2

22-

Nov

22-

Nov

28-

Nov

1.20 0.20 0.20 0.20 0.20

Final Mock

Test

28-

Nov

28-

Nov

28-

Nov

1 0.15 0.15 0.15 0.15

20

21