Progress Report 1

Busick MWF 8:00 am

Group D

Members:

William Klotnia
Joel Goodwin
Sean Koch
Connor Lenartowicz

February 9, 2018

Backward Looking Summary

Each preliminary research and development exercise was completed for the purpose of gaining experience and knowledge with the AEV hardware, the arduino software, and learning different techniques for proper creative designing.

Exercise one involved a series of steps to gain understanding for some equipment that is crucial to the AEV project and its success, such as connecting the motors to the Arduino, setting up the Arduino software, and uploading code using some of the preinstalled functions. This code involves accelerating, decelerating, and holding speed amounts of the motors for different time intervals. Upon the initialization of this code, there was some resistance at first. The initial half of a second resulted in the motor trying to spin the blade, but being ineffective. This resistance most likely occurred from the low power levels. When the Arduino was programmed to have more motor power, this problem did not occur.

Exercise two involved the introduction and familiarization of the reflectance sensors that help the AEV record the distance that it travels. The accuracy of these sensors and understanding their use will impact how precise the AEV is in completing its multiple tasks such as traveling the correct distance to wait at the gate, and then continuing the correct distance to attach to caboose. This exercise also illustrated potential problems or limitations to the effectiveness of each command. The brake command may cut power to the motors, however the Arduino will still continue down the track for a little bit after the power has been cut. Additionally, the “celerate” function does accelerate the AEV at a consistent rate. These are examples of potential problems that will need to be addressed in the final build of the AEV.

Exercise three was a brainstorming session for creating individual and eventually a group concept design for the AEV. Each design differed, with some being oriented primarily horizontally while others were vertical. The orientation of the propellers also differed. The top wheel brace was also something that differed from concept to concept, with some opting for the L shaped one, hoping for lengthwise stability and others opting for the more compact design to reduce any unwanted movement of the AEV overall.

Figure 1: Design 2

The vertical design allows for substantial aerodynamics for the vehicles. The arduino is centered on the plus-sign base which allows for an even distribution of weight across the vehicle for enhanced stability. The vehicle utilizes a front and back facing motor which allows for the vehicle to propel in either direction without the need for the reversal of motors. This could also act as a makeshift braking system. After reviewing the bill of materials, the estimated cost of this design would be $170, with the arduino being more than half of the cost.

Figure 2: Design 4

The middle cross baseplate will allows the AEV to be more stable and have more space for cargo carrying. The outstretched sides of the baseplate allow for no extra pieces to hold the motors far enough from the body to work. Similar to the example AEV design, the horizontal design will prove to be more stable and efficient in the long run. This design will cost an estimated $180, depending on how many small parts are used to hold together the frame.

Figure 3: Design 3

The T-shape allows the propellers the space they need to operate without adding the extra mass that the attached wings add. By keeping the base flat, the AEV will still have space to carry a payload. The placement of the battery, motor, and wheel bracket should distribute the weight evenly, while keeping the Arduino the required distance away from the magnet. This design costs only about $160-$165 accordingly.

Figure 4: Design 1

The vertical design of the AEV allows for the AEV to be aerodynamic and cut through the air as it moves. It features two front facing propellers, one on top of the other which allows for the AEV to move at a faster rate in comparison to the base design. With it being vertical, the weight is evenly distributed across it, allowing for optimal stability. This design’s estimated cost is near $170.

Discussion proved to be the most effective brainstorming technique for figuring out what the group concept sketch should look like.

Figure 5: Group Design

The group concept sketch combined many positive aspects of each individual design. The horizontal design, while not quite as stable as the vertical design, was decided to be the best mix between stable and cargo carrying capacity. The T letter design provides the least cluttered design and saves on cost and weight by having areas for the propellers without external or additive parts required. The brace chosen is the most compact and the most stable. The estimated cost of this design is around $175.

Exercise 4 focused on the introduction of the data analysis tool and its potential uses for identifying trends and results from different AEV runs. The exercise itself required the MATLAB tool to be used to compile data with respect to power vs. time and power vs. distance that was collected from a run of the AEV following a specific set of code.

Figures 6 & 7: Power vs. Time and Distance vs. Power Graphs

In the first three seconds of the code running, the motors accelerated to 25% power. The motors were then set to run at the same power for one second. For the following two seconds, the motors ran at 20% power. After this, the motors were reversed and ran at a constant 25% power for two seconds. Finally, the brake command was issued to cut power to the AEV’s motors. Future data gathered and compiled using the data analysis tool can be used for further research and development in a multitude of different areas.

For Exercise 5, each group member’s design was graded by the team and scored on criteria crucial to the success of the AEV. This allowed the team to make educated decisions upon how to continue the AEV project and which design to use. The following tables show two ways in which the team graded each design model.

Table 1: AEV Concept Screening Matrix

Table 2: AEV Concept Scoring Matrix

In the first table, AEV designs were scored on a plus-minus-zero scale. Where a plus represents a design that is better than the reference design in a certain criteria, a negative then being worse than the reference, and a zero being equal or near even with the reference design. The reference to which all designs were compared was the initial design made following the lab procedures in Lab 01 and 02. This design is rather standard in all AEV features that could affect success criteria., which is why it was chosen as a reference.

The second table shows a more complex way to grade designs, in which each scoring criteria was given a weight based off how vital it is to the success of the AEV. Then, each design was rated on a scale of 1-5 for each criteria; the weight of the criteria was then multiplied by this score in order to create a weighted score. All weighted scores for criteria were then summed to see which design had the overall best score.

Using these two scoring tables, the designs that were kept were designs one and three. These designs were chosen because in both the concept screening and the concept scoring, they earned the highest scores. The two lowest scoring designs were then scrapped.

The criteria used in both tables were stability, capacity to carry, durability, safety, and speed/aerodynamics. Stability represents the balance of an AEV. Will it stand upright on the track or not? It is the core property of the AEV. If the design does not display a smooth, even balance, it is likely that the AEV will be slowed down by unwanted friction due to excess tilting, or it may fall off the track entirely. Capacity to carry is what it sounds like. Is there adequate space on the design for weight storage? Durability is the design’s capability to endure. Will the design be able to make the entire route or not? Safety represents how safe the design can hold weight. This is very similar to carrying capacity, although it is different in the way that carrying capacity is the vehicle’s capacity to carry based of off space, whereas safety is the vehicle’s capacity based off of weight. Can the design hold and support weight or not? Lastly, speed/aerodynamics is how fast the AEV design can go. Are the turbines faced in a position that will improve the speed of the vehicle? And are there certain characteristics such as wing blades that may increase the speed as well? All of these criteria were taken into account, with all of those questions being asked for each design during the design process.

After thoroughly grading each design, many insights were gained. Design 1’s sleek vertical body and same-facing turbines provides for faster transportation and good stability, although it does not have much room to carry weight. These attributes can be seen in the graded tables above. Design 2 features a similar stable vertical design, yet it has turbines facing opposite directions, and also does not have much room to support weight. Design 3 has much more room to hold and support weight, however, this bulkier design is more likely to be slower. Design 4 is also a more bulky, horizontal design that can support weight, yet there is a high likelihood of low stability due to too much weight needing to be balanced. All in all, the results of the screening scoring sheets are clear. Design 1 and 3 stood out in the initial screening, and those same two designs scored highest in the second round of grading as well. The team will move forward focusing on these two designs.

Forward Looking Summary

Moving forward, the group looks to improve upon current designs and decide upon a final design for the AEV that maximizes the decided-upon criteria. In order to complete this, the group will run tests that show which features show the best capabilities in the areas that were decided upon by the group to be the most important. By testing the features to see which perform the best, the group will have numerical data to ensure that the most efficient design is chosen. One topic that is to be investigated is propeller configuration. In this lab differents propeller setups will be tested in order to decide the most efficient configuration for the final AEV design in regards to both power consumption and speed. In another lab, power braking will be tested in order to determine the most efficient and consistent way to stop the AEV at the desired point on the track.

Following the Advanced R&D stage, the group plans to implement what is learned from the labs. Another priority is to research the use of the servo as a brake. Though power braking itself is on of the aspects to be tested, that is to be done by reversing thrust of the motors. Further experimentation must be done in order to determine which kind of braking stops the AEV in the most efficiently.

Further goals moving forward are to ensure that the features of the final AEV design allow the AEV to be successful in its mission and allow it to do so efficiently. The group hopes to determine the features that allow for maximum payload, efficiency, speed, and aerodynamics.

Testing for these components will be done in upcoming Advanced R&D labs.

List of upcoming goals and tasks:

Decide upon primary designs to be tested.

Which features of Design 1 and 3 specifically will be used?

Research power braking vs. coasting.

Differences in stopping distance and time between coasting to a stop and braking with propellers

Research propeller configuration.

Test variables such as number of propeller as well as direction of propeller.

Research Servo function.

How can we attach the Servo and what piece can we use to rotate with it as an applied brake?

Assemble AEV design.

Carry out the best fit design as decided upon in Exercise 5.

In order to achieve these goals, the team has put in place a working schedule along with assigning tasks for each group member. Tasks will be completed primarily in lab during class with the provided materials. If time is running short, the team will schedule additional meetings outside of class in order to complete any and all remaining work. Typically, the group works together to complete a task, with each member contributing in different ways towards the task. For these upcoming tasks specifically, Connor will be in charge of writing code whereas Will and Joel will be in charge of AEV assembly, and Sean will handle website updates and test runs of the AEV. The team will continue with the tasks stated above in the order of which they are stated.

Appendix

Team Meeting 1: 2/7/18

Location: Hitchcock Hall – 8:00am

Attendees:

Sean Koch
Connor Lenartowicz
Will Klotnia
Joel Goodwin

Objective: Brainstorm final AEV design

Tasks:

Compare individual designs
Explain reasoning behind designs
Discuss which aspects of each design could be carried over
Which aspects will make the AEV more efficient?
Which AEV will be most capable of carrying a load?

Arduino Code:

Exercise 1 Code:

// Accelerate motor 1 from start to 15% power in 2.5 seconds

celerate(1,0,15,2.5);

// Run motor one at 15% power

motorSpeed(1,15);

// Run previous command for 1 second

goFor(1);

// Accelerate motor 2 from start to 27% power in 4 seconds

celerate(2,0,27,4);

// Run motor 2 at 27% power

motorSpeed(2,27);

// Run previous command for 2.7 seconds

goFor(2.7);

// Decelerate motor 1 from 27% to 15% power in 1 second

celerate(1,27,15,1);

// Brake motor 2

brake(2);

// Reverse motor 2

reverse(2);

// Accelerate motor 2 from start to 31% power in 2 seconds

celerate(2,0,31,2);

// Run all motors at 35% power

motorSpeed(4,35);

// Run previous command for 1 second

goFor(1);

// Brake motor 2

brake(2);

// Run motor 1 at 35% power

motorSpeed(1,35);

// Run previous command for 3 seconds

goFor(3);

// Brake all motors

brake(4);

// Run previous command for 1 second

goFor(1);

// Reverse motor 1

reverse(1);

// Accelerate motor 1 from start to 19% power in 2 seconds

celerate(1,0,19,2);

// Run motor 2 at 35% power

motorSpeed(2,35);

// Run previous command for 3 seconds

goFor(3);

// Run motor 1 at 19% power

motorSpeed(1,19);

// Run previous command for 3 seconds

goFor(3);

// Run all motors at 19% power

motorSpeed(4,19);

// Run previous command for two seconds

goFor(2);

// Decelerates all motors from 19% to 0% power in 3 seconds

celerate(4,19,0,3);

// Brake all motors

brake(4);

Exercise 2 Code:

// Run Reflectance Sensor Test

reflectanceSensorTest();

// Run all motors at 25% power

motorSpeed(4,25);

// Run previous command for 2 seconds

goFor(2);

// Run all motors at 20% power

motorSpeed(4,20);

// Run previous command for 12 feet

goToAbsolutePosition(295);

// Reverse all motors

reverse(4);

// Run all motors at 30% power

motorSpeed(4,30);

// Run previous command for 1.5 seconds

goFor(1.5);

// Brake all motors

brake(4);

Exercise 4 Code:

// Accelerates all the motors from start to 25% power in 3 seconds

celerate(4,0,25,3);

// Runs all the motors at 25% power

motorSpeed(4,25);

// Runs the previous command for 1 second

goFor(1);

// Runs all the motors at 20% power

motorSpeed(4,20);

// Runs the previous command for 2 seconds

goFor(2);

// Reverses all the motors

reverse(4);

// Runs all the motors at 25% power

motorSpeed(4,25);

// Runs the previous command for 2 seconds

goFor(2);

// Cuts the power to all the motors

brake(4);