Deliverables (N)

Lab 1:

Glossary of Basic Function Calls

  1. celerate(m,p1,p2,t); —– Accelerates or decelerates motor(s) m from start speed (%) p1to end speed (%) p2 over a duration of t seconds.
  2. motorSpeed(m,p); ——- Initializes motor(s) m at percent power p
  3. goFor(t);——— Runs the motor(s) at their initialized state fort  seconds
  4. brake(m);——– Brakes motor(s) m. Note: This does NOT brake the AEV, just stops the motors from spinning.
  5. reverse(m);——- Reverses the polarity of motor(s) m
  6. goToRelativePosition(n);—— Continues the previous command for n marks from the vehicle’s current position. n can be positive or negative, with positive meaning the vehicle is moving forward, negative meaning the vehicle is moving backward
  7. goToAbsolutePosition(n);——- Continues the previous command for n marks relative to the overall starting position of the AEV

 

Lab 2:

Reflectance Sensors

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

The function of reflectance sensors is to determine one full wheel revolution.

Using reflectance sensors and calculation, we can set the distance for AEV to travel precisely.

 

 

Lab 3:

Team’s Sketch

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Our concept is to make the AEV as small as possible. We believe that by reducing the size, the weight of the AEV is also smaller, so our AEV can travel faster efficiently with a good power to speed ratio. The air resistance of the AEV also become smaller by reducing the size to the AEV.

 

 

Aw Tai Wei’s Sketch

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Raquel’s Sketch

Brandi’s Sketch

Divyaam’s sketch

Lab 4:

Power v Distance:


Depicts deceleration to reverse followed by constant power to maintain that speed. This plot represented the power used up depending on the time consumed.

Power v Time:

Was seen to have an initial steep slope as well for the first three seconds which depicts the power delivered for the AEV to accelerate followed by which power is supplied constantly to maintain a 25% speed and then a steep slope.

Lab 5:

Concept Screening and Scoring

The spreadsheets represent a good comparison of the given AEV designs and depicts where certain designs lack while where they are perfect and a comparison like this not only helps choose which AEV to go forward with, it also provides a good idea of what kind of developments need to be made in the given AEVs.

The matrices show that developing Design A and Design D in order to ensure that its strengths have been taken into account would be the best way to go forward. Both of the designs are better overall than the other designs and would be the easiest and the best option for further development. Design A has the most streamlined shape and the best size depicted in order to work as efficiently as possible. Design A lacks in durability considering its design which would be difficult to maintain. Design B is durable and safe however is really big and doesn’t have a streamlined shape to minimize drag. Design C is stable however looks a little too small which makes everything fitting on the AEV at the required distances difficult and hence isn’t safe. Design D has a stable design and a good size however is not a perfectly streamlined shape to minimize drag. Design A and D would be carried forward in the design cycle and could produce great results.

Advanced Research and Development:

Approach and Overview:

  • Our company and team wanted to choose different objectives that would help determine the best possible way to make the AEVs the most efficient
  • When deciding on what topics to work on, Group N decided to choose two that went along with each other that way we would be able to assess the data and compare the results.
    • The ability to compare results then would allow for us to come up with the best AEV design and power usage.
  • The two Advanced Research and Development Topics our team worked on were Battery Testing & Motor Configuration.

Battery testing:

  • Tested 2 different fully charged batteries
    • Voltage difference between individual batteries
  • 1. Obtained 2 fresh batteries
  • 2. Performed 4 trial runs on battery #1
    • Recorded distance and power
  • 3. Performed 2 trial runs on battery #2
    • Recorded distance and power
  • 4. Tested different code using battery #2
    • Replaced goFor(2) with goToRelativePosition(-100);

Motor Configuration:

  • Tested a large number of configurations of where the motors were located.
    • On the following slides the different configurations are shown along with a basic understanding of how they affected the distance traveled on the track.
  • For our group we wanted to test our original team design and then rearrange where the motors were located to see if there were any changes in the distance the AEV traveled or use of power.
  • It turned out that one of our “random” designs that we did just for fun, ended up being the most efficient with power and most accurate with breaking too.

Limitations and Summary:

  • We had encountered a few problems during the testing.  At first, the code which we used for testing, was not well tuned and caused the AEV flew off the track.
  • During the motor configuration 2 trial 2, our AEV hit something on the track and slowed down. This affected the accuracy of our results.
  • Besides, there were a few times that the propellers of our AEV hit the wires which are connected from the Arduino Board to the battery. This also reduced the consistency of our results.
  • After a few times of trials and changes, we managed to come out a better coding for our testing. Before carrying out the test run, we will check the presence of obstacle on the track. We also ensure that all wires are tied properly to prevent the propeller from hitting the wire again.

Plan looking forward:

  • In the upcoming labs we will be working on Performance Testing.
  • Consistency in stopping
  • Maximizing power efficiency
  • Find the most efficient way to travel in the return portion of the objective with the caboose
  • Consistency in results
  • Minimizing material use
  • Incorporating all team members ideas

Final Sales Pitch

Our AEV design is superior to the other groups because it had reliable and complete stopping which allows the AEV to go faster without having to worry about how the AEV is going to stop. Our AEV was below the class average on ever aspect.

Our AEV PT data:

  • Capital $161,350
  • Energy (J) 204.214
  • Time (s) 43.5
  • Total Cost $543,707

Class average PT data:

  • Capital $161,737
  • Energy (J) 219.2
  • Time (s) 51.72
  • Total Cost $571,450

As you can see our AEV was faster and cheaper and used less energy compared to the class. The design is sleek and sturdy which helps maintain a reliable outcome during each run.