MCS 275 Spring 2023 Homework 4¶
- Course Instructor: Emily Dumas
Instructions:¶
- Complete the problems below, which ask you to write Python scripts.
- Upload your python code directly to gradescope, i.e. upload the
.pyfiles containing your work.
Deadline¶
This homework assignment must be submitted in Gradescope by Noon central time on Tuesday February 7, 2023.
Collaboration¶
Collaboration is prohibited, and you may only access resources (books, online, etc.) listed below.
Content¶
This assignment is about object-oriented programming, especially subclasses and inheritance. It focuses on the material of worksheet 4.
Resources you may consult¶
Most relevant:
- Worksheet 4 Solutions
- Lecture 4 - Operator Overloading
- Lecture 5 - Inheritance
- Lecture 6 - Inheritance part 2
- Examples from our sample code repository
Less likely to be relevant, but also allowed:
- Any other lecture of MCS 275 spring 2023.
- Python tour
- Slides from any lecture of my most recent MCS 260 course MCS 260, Fall 2021.
- Any lecture video posted on our course Blackboard site
- Downey's book
- Any other textbook listed on the course blackboard site under "Textbooks"
Point distribution¶
This homework assignment has 2 problems, numbered 2 and 3. The grading breakdown is:
| Points | Item |
|---|---|
| 3 | Autograder |
| 6 | Problem 2 |
| 6 | Problem 3 |
| 15 | Total |
The part marked "autograder" reflects points assigned to your submission based on some simple automated checks for Python syntax, etc. The result of these checks is shown immediately after you submit.
What to do if you're stuck¶
Ask your instructor or TA a question by email, in office hours, or on discord.
Problem 1 doesn't exist¶
In Gradescope, the score assigned to your homework submission by the autograder (checking for syntax and docstrings) will be recorded as "Problem 1". Therefore, the numbering of the actual problems begins with 2.
This will happen on every assignment this semester. Starting on the next homework, I won't comment on this practice in the homework, and will just omit problem 1.
Get bots.py¶
Both problems on this assignment involve adding subclasses to bots.py. So you'll need to grab a copy of that example module to modify in this project:
The second file (plane.py) is used by bots.py but won't be modified for this assignment.
When you submit this homework, upload the modified file bots.py and nothing else.
Problem 2: HoverBot¶
Make a subclass HoverBot of Bot whose constructor expects to be given another Bot instance (the target), and which then always makes sure it remains near that robot, but moves randomly around it. So it "hovers" near a given robot.
Specifically, "hovering" should mean that the target robot and the HoverBot always have positions that differ by one of $\langle 1,0\rangle$, $\langle -1,0\rangle$, $\langle 0,1\rangle$, $\langle 0,-1\rangle$. The HoverBot constructor should accept an argument target that is expected to be an instance of Bot. This argument should be stored as an instance attribute (self.target).
Any time the HoverBot needs to make a decision about its position (e.g. in the constructor or in update), it should check the target position and add a random vector from the list given above to decide on its own position. It should not modify the target robot at all.
Example of HoverBot behavior¶
Here's an example of how HoverBot should behave. In this case it is asked to hover near a PatrolBot. Notice the two move around but their positions always differ by $\langle 1,0\rangle$, $\langle -1,0\rangle$, $\langle 0,1\rangle$, or $\langle 0,-1\rangle$.
import bots
import plane
B1 = bots.PatrolBot(
position=plane.Point2(0,0),
direction=plane.Vector2(2,1),
n=10
)
B2 = bots.HoverBot(target=B1)
for _ in range(10):
print("PatrolBot at {}".format(B1.position))
print("HoverBot at {}".format(B2.position))
print("Difference={}\n".format(B2.position-B1.position))
B1.update()
B2.update()
Problem 3: NoBacktrackWanderBot¶
Add a subclass NoBacktrackWanderBot that behaves similarly to WanderBot, taking one random step up, down, left, or right on each step. However, this class has one essential difference: The step it chooses will never be the exact opposite of the step it took last time.
That is:
- On the first call to
update, this bot does exactly the same thing as aWanderBot - On any subsequent call to
update, this bot knows the stepvit took last time, and will choose a random step among the ones used byWanderBotthat are not equal to-v.- e.g. if on step 1 the robot chooses $\langle 1,0\rangle$, then on step 2 it will choose one of $\langle 1,0\rangle$, $\langle 0,1\rangle$, $\langle 0,-1\rangle$ at random.
Note: This class can inherit directly from Bot or from any subclass thereof as you deem appropriate.
Example of NoBacktrackWanderBot behavior¶
Here's an example of a short program that simulates a WanderBot and which points out each time the robot backtracks on its last step.
import bots
import plane
B = bots.WanderBot(position=plane.Point2(0,0))
position_log = []
print(B.__class__.__name__ + ":")
for _ in range(10):
position_log.append(B.position)
if len(position_log) > 2 and position_log[-3]==position_log[-1]:
print(" ", B.position," (backtracked!)")
else:
print(" ", B.position)
B.update()
And here is the same program using a NoBacktrackWanderBot. Note that no backtracking steps are detected.
import bots
import plane
B = bots.NoBacktrackWanderBot(position=plane.Point2(0,0))
position_log = []
print(B.__class__.__name__ + ":")
for _ in range(10):
position_log.append(B.position)
if len(position_log) > 2 and position_log[-3]==position_log[-1]:
print(" ", B.position," (backtracked!)")
else:
print(" ", B.position)
B.update()
Epilogue¶
With the completion of this assignment, our work on the robot simulation in MCS 275 is finally done! To celebrate, here's a picture created by the AI image generator DALL-E 2 in response to the prompt
A bunch of happy robots walking off into the distance with a sunset behind them digital art
Revision history¶
- 2023-02-02 Initial publication