2021/10/26 下午1:51 Project 2021: ELEC1601/ELEC9601 Introduction to Computer Systems
https://canvas.sydney.edu.au/courses/35882/pages/project-2021 1/8
Project 2021
Group Project Padlet links:
Week 9 : Setting up software and Trello
(https://sydney.padlet.org/docpeterjones/nrxxd04yofbtb4fa)
Weeks 10-12: Working on project
(https://sydney.padlet.org/docpeterjones/q5jy5rwcc9aqbrvz)
Problem Specification
Project 2021 Rules list (will be regularly updated based on Ed Discussions)
(https://canvas.sydney.edu.au/courses/35882/pages/project-2021-rules-slash-faq-slash-common-
queries)
https://sydney.padlet.org/docpeterjones/nrxxd04yofbtb4fa
https://sydney.padlet.org/docpeterjones/q5jy5rwcc9aqbrvz
https://canvas.sydney.edu.au/courses/35882/pages/project-2021-rules-slash-faq-slash-common-queries
2021/10/26 下午1:51 Project 2021: ELEC1601/ELEC9601 Introduction to Computer Systems
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Your goal is to develop a robot that can navigate through a maze. This is an example of the maze
we used during the physical labs.
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2021/10/26 下午1:51 Project 2021: ELEC1601/ELEC9601 Introduction to Computer Systems
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Unfortunately, we’re online and don’t (or at least not all of us) have a robot! Instead, we’re doing a
software simulation:
2021/10/26 下午1:51 Project 2021: ELEC1601/ELEC9601 Introduction to Computer Systems
https://canvas.sydney.edu.au/courses/35882/pages/project-2021 4/8
(Note it is deliberately basic to cut down on code, but you can improve it (see marks below)
The robot is designed with two forward facing sensors (each a basic representation of a
Photocell (light sensor) (https://canvas.sydney.edu.au/courses/35882/pages/11-dot-2-8-
connecting-a-light-sensor) ). They have a short range, but will return a stronger value when the
robot is closer to a wall.
Initial design goals
https://canvas.sydney.edu.au/courses/35882/pages/11-dot-2-8-connecting-a-light-sensor
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Design code to automatically navigate this (or any other user created maze) in a reasonable time.
In your final week, we will give you a new one to test that your code works!
Secondary design goals
After completing the basic task, you can follow either (or both) of the following directions in
searching for better marks. Note you can split up your group to focus on different tasks:
Some example challenges:
Your robot may, or may not, face a number of different challenges. Think about how you would
arrange your sensor/s and code your robot to deal with each potential challenge.
Software focused direction: make your simulated robot as fast/robust/impressive as possible.
Some ideas:
be able to navigate any maze quickly
create new mazes, make them available by Ed, compete with your peers. Some example
challenges (from the physical lab) that you could incorporate (think about how you would
arrange your sensor/s and code your robot to deal with each potential challenge. You can
add sensors if you wish):
Entry with
straight section
90 degree turn T-junction Side branch Dead end
Robot travels in
a straight line
with no
obstacles
Robot must
negotiate a 90
degree turn
either left or
right
Robot can
choose to turn
left or right. We
would make
sure that either
choice is equally
fair.
Robot can
choose to turn
or to continue
straight ahead.
We would make
sure that either
choice is equally
fair.
This will be the
object collection
point and will be
the only dead
end in the
maze.
thinner gaps between walls (trickier to navigate – always bumping into each other)
wider walls (let robots try to move super quickly through the maze)
curved walls
Make the GUI not so ugly!
2021/10/26 下午1:51 Project 2021: ELEC1601/ELEC9601 Introduction to Computer Systems
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change it from a maze navigation robot to a line tracking robot
Hardware focused direction: using everything you have learnt in the labs thus far, show how
your navigation code can be integrated with sensors. This should give the demonstrators a
high degree of confidence that should you connect it with real hardware it would not take long
to get it actually working. Ideas:
complete Lab 7 2021 (https://canvas.sydney.edu.au/courses/35882/pages/lab-7-2021-robot-
sensing-and-vision-robot-navigation) and integrate your navigation code into this. Show
what actions your robot takes as the sensors are triggered in TinkerCad
incorporate more realistic physical models (acceleration, momentum)
incorporate remote communication to robot ( Lab 6 2021
(https://canvas.sydney.edu.au/courses/35882/pages/lab-6-2021-serial-communication-and-
data-transfer-robot-bluetooth) ) to simulate manual remote control in addition to automatic
navigation mode.
run on physical robot if you have one! (must be largely same code – we don’t want to see
plagiarism)
Report initial details
Details will change – we want to see how you progress with the labs first. However, one critical
aspect will be to discuss the challenges you expect in moving your software based design to
working hardware design. This is to make you think about everything you learnt in the labs over
the duration of this course.
Following the hardware focused path and completing Lab 7 2021
(https://canvas.sydney.edu.au/courses/35882/pages/lab-7-2021-robot-sensing-and-vision-robot-
navigation) may help you here.
Setting up the template:
https://canvas.sydney.edu.au/courses/35882/pages/lab-7-2021-robot-sensing-and-vision-robot-navigation
https://canvas.sydney.edu.au/courses/35882/pages/lab-6-2021-serial-communication-and-data-transfer-robot-bluetooth
https://canvas.sydney.edu.au/courses/35882/pages/lab-7-2021-robot-sensing-and-vision-robot-navigation
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Project Software Install Instructions
(https://canvas.sydney.edu.au/courses/35882/pages/project-software-install-
instructions)
Approximate rubric
This is only a guide, demonstrators will judge where projects lie in the final (demonstration) lab
session:
85/100 (HD) 75/100 (D) 65/100 (Credit) 50/100 (Pass)
Everything for
distinction plus:
High quality line
tracking as well
(subject to maximum
amount of curve)
Realistic physical
models
Demonstration of how
code can be calibrated
Other hardware
integration
Impressive GUI
High quality maze
navigation (in
software). Including
ability to navigate more
complex mazes
Software robot shows
realistic link to
hardware (integrated
your navigation code
with sensors in
TinkerCad).
Have contributed (and
your robot can solve)
shared maze
challenges through Ed.
Software robot can
navigate a range of
mazes quickly
(standard width to
travel in, right angle
turns only)
Software robot can
automatically (no hard
coding) navigate initial
(provided) maze within
1 minute.
Bonus Marks. We will select a range of mazes (user-submitted or designed by us) for demo day.
Fastest completion times will be recorded.
There will be two categories: basic mazes and complex mazes.
For basic mazes
https://canvas.sydney.edu.au/courses/35882/pages/project-software-install-instructions
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Top 25%:5 marks; (that is, your time is faster than 75% of the class)
Top 10%: 7 marks;
Top 3: 10 marks.
For complex mazes
Top 25%: 5 marks; (that is, your time is faster than 75% of the class)
Top 10%: 10 marks;
Top 3: 15 marks.
You receive the maximum of these two bonus scores.
The competition bonus marks will include the whole 2021 Semester 2 class (all groups, all lab
sessions).
(Yes, you can score over 100%. This can make up shortfalls elsewhere in your course – mid sem
test, lab completion or lab report.)
https://canvas.sydney.edu.au/courses/4901/pages/zoom