Shortest Path


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BBM204 Software Practicum II
Topics: Graphs – Shortest Path, Regular Expressions, Greedy Programming
Programming Language: Java (OpenJDK 11)
Türkiye’s First Sub-Meter High-Resolution Observation Satellite
İMECE, Türkiye’s first sub-meter high-resolution observation satellite, has recently been launched with
the goal of capturing detailed images for a variety of applications. The satellite is equipped with a powerful
camera that could capture images with unprecedented detail, allowing researchers to monitor everything
from natural disasters to urban development.
With the launch of İMECE, Türkiye made its
space history by launching for the first time an
electro-optical satellite camera with a sub-meter
resolution that was developed using only domestic and national resources. İMECE will simultaneously orbit the sun at an altitude of 680 kilometers, meeting Türkiye’s need for high-resolution
satellite imagery. The satellite, capable of capturing images from any location in the world
without any geographical limitations, will benefit
Türkiye in various ways, including target detection and identification, natural disasters, mapping, and agricultural practices.
As the students of HUBBM and HUAIN, two
of the best computer engineering departments
in the country, you have been selected to implement some of the most important algorithms
that İMECE will use for a number of applications. Your algorithms will be used to update the
existing algorithms onboard İMECE to optimize
several of its missions.
The success of this mission depends on your algorithms – are you up to the challenge?
1 Reading the Input Data Using Regular Expressions
The necessary input parameters for this assignment are given in a .dat file as the first command line
argument, and it will be structured as follows:
grid_input_file_name = “Map_480x480.dat”
num_rows = 480
num_cols= 480
mission_0_source = ( 300,100)
mission_0_destination= (400, 400 )
mission_1_source =( 150 ,300 )
max_flying_height= 4000
fuel_cost_per_unit= 0.1
Note that the order of variables can change along with the order and the placement of the whitespace
characters (tabs or spaces) around the variable names, equal sign, comma, parentheses and outside
of quotes. To compensate for those changes easily, you are supposed to implement this part using several
regular expressions. To read the necessary parameters for this mission, you must complete four methods
in DatReader class from the starter code. One of those methods is given below as an example and guidance
on how to implement this part.
1 public int getIntVar(String varName) {
2 Pattern p = Pattern.compile(“[\\t ]*” + varName + “[\\t ]*=[\\t ]*([0-9]+)”);
3 Matcher m = p.matcher(fileContent);
4 m.find();
5 return Integer.parseInt(;
6 }
The first line of the method contains the necessary regular expression for matching an integer variable
with an optional number and placement of whitespace characters. Using the variable num_rows as an
example, the figure below illustrates the match and the groups (blue, encompassing the whole match, is
Group 0, while green, encompassing only the relevant part in parentheses, is Group 1).
A logic similar to the example getIntVar() method should be used for other methods while parsing the
input file.
In the getStringVar() method, you should alter the given regular expression such that it matches the value
of the variable given by varName as Group 1 and returns it.
In the getDoubleVar() method, you should alter the given regular expression such that it matches the
value of the variable given by varName as Group 1, parses it as a Double and returns it. Your regular
expression should support floating point numbers with an arbitrary number of decimals or without any
(e.g. 5, 5.2, 5.02, 5.0002, etc.).
In the getPointVar() method, you should alter the given regular expression such that it matches the value
of the variable given by varName as two integers between parentheses separated by a comma as Group 1
and Group 2 respectively. Your method should then create an instance of the Point class by two integers
parsed using Group 1 and Group 2, then return the Point instance.
2 Mission 0 – Finding the Most Cost-Efficient Path
You have been selected by the İMECE developers team to contribute to the implementation of some very
important functionalities of the satellite. In this first mission, you are expected to implement methods
that calculate and draw the most cost-efficient path to be taken by a surveillance drone over a
geographical area.
2.1 Background Information and Mission Objectives
You will be given topographic data of a geographical area as a 2D array of land elevation values (integers).
The unit of the elevation data is not relevant to this mission, but you may think of it as the land height in
meters. Your objective is to find the most cost-efficient path between two coordinate points (source and
destination), which will be used by a surveillance drone. The drone has a limited power supply needed for
extending the maximum flight time before the next refueling, so the satellite will perform the calculations
given the topographic data, and feed the calculated path data to the drone.
There are some important constraints that need to be considered while calculating the path:
• A drone can move only in a specific way: if we assume that the current position of the drone is a
specific pixel on the given map, the drone’s next move can only be performed towards one of the
neighboring pixels.
E.g., if we consider the map to be positioned such that the top is North, the bottom is South, the
left is West, and the right is East, a drone can only move towards eight directions at most (even
less if the current position is at an edge of the map): E, W, N, S, SE, SW, NE, NW.
• A drone has a maximum height it can fly over and it will be given as an input. If the land height at
any point is higher than that maximum height, then the drone cannot move in that direction. This
is illustrated in the figure below. Here, the drone is at the bottom edge of the map (cannot move to
directions SW, S, SE), and its maximum flying height is 100. Therefore, the only possible moves
for the drone are W, NW, NE, E.
• Drones use fuel to fly. Flying in a straight line causes uniform fuel consumption. However, if a drone
needs to go upward as well when the next point on the map is higher than the current position,
the fuel consumption increases. Our goal is to find a path for a drone such that it will be the most
cost-effective based on the formula given below, which calculates the total cost of moving from a
source point (xs, ys) to an end point (xe, ye):
costs,e = (Dists,e × fuelCostP erUnit) + (climbingCostP erUnit × heightImpact)
Distance between a start point (xs, ys) to an end point (xe, ye), denoted as Dists,e, is calculated
as the Euclidean distance between two neighboring pixels given their x and y coordinates. The cost
of fuel per distance unit and the cost of elevation (climbing) will be given as inputs. Finally, the
impact of the point height heightImpact will be calculated as follows:
heightImpact =

0, if height(s) ≥ height(e)
height(e) − height(s), otherwise
Mission Objective:
Find and draw the most cost-efficient path from a source to a destination point
(pixel), which will be taken by a surveillance drone over a geographical area.
2.2 Input File Format
The input file with the 2D geographic elevation data will be given in the .dat format and its name (as well
as all other input parameters) will be extracted from the input file given as first command line argument.
Your program should parse the elevation values for each pixel given in the file and use it to initialize the
grid instance variable of the IMECEPathFinder class. A sample input file is given as Map_480x480.dat.
An excerpt from the sample input file is illustrated below (only numbers).
x coordinates: 0 1 2 3 4 5 6 7 8 9 . . .
y coordinates: |
0 | 2537 2483 2475 2480 2518 2532 2480 2478 2431 2428 . . .
1 | 2541 2549 2614 2700 2647 2746 2690 2621 2550 2487 . . .
2 | 2525 2525 2640 2769 2802 2883 2856 2694 2631 2574 . . .
3 | 2514 2505 2526 2614 2717 2715 2867 2836 2771 2644 . . .
4 | 2506 2482 2480 2528 2518 2561 2586 2662 2654 2593 . . .
. . . . . . . . . . .
. . . . . . . . . . .
. . . . . . . . . . .
Note that the pixel coordinates are to be considered as follows: (0, 0) is the top leftmost point with an
elevation of 2537. Its reachable neighbors are pixels (1, 0) to the E with an elevation of 2483, (1, 1) to
the SE with an elevation of 2549, and (0, 1) to the S with an elevation of 2541.
2.3 Expected Solution and Output Format
For the given sample input map file,
the maximum flying height of 4000
meters, the source point with coordinates (300, 100), and the destination
point with coordinates (400, 400), the
expected most cost-efficient path (green
pixels) is depicted in the figure on the
If you inspect the solution, you may observe that the height of the selected
points (pixels) along the path never exceeds the maximum flying height of the
drone. Moreover, while calculating the
best path, the ability of a drone to move
into only adjacent pixels (points) at each
step is taken into consideration. A drone
never takes a step from its current position to a point (pixel) that is not a direct neighbor of its current point. This
explains why we don’t see a straighter
You are expected to complete:
• drawGrayscaleMap(), getMostEfficientPath(), getMostEfficientPathCost(),
and drawMostEfficientPath() methods of the IMECEPathFinder class.
You are expected to produce two outcomes for this part of the assignment: an output file and a STDOUT
output. The DrawingPanel class will be used for drawing the map in this assignment (a very useful Java
class from Building Java Programs by Reges and Stepp). To draw a grayscale map of the given land area
as illustrated in the first figure of this mission, you need to draw a grid using the Graphics object obtained
by getGraphics() method from the DrawingPanel class. The map colors should be grayscale values in
the range [0, 255], scaled based on minimum and maximum elevation values in the given grid.
The output file must be named grayscaleMap.dat and it should contain a grayscale value for each pixel (do
not mark the path pixels in any special way!) in a 2D format just like the input file. These greyscale
values are the values used to draw the map. An excerpt from the expected output file is illustrated
102 98 97 97 101 102 97 97 93 93 94 …
103 103 109 116 111 120 115 109 103 …
101 101 111 122 125 131 129 115 110 …
100 99 101 109 117 117 130 128 122 …
100 97 97 101 101 104 106 113 112 …
. . . . . . . . .
. . . . . . . . .
. . . . . . . . .
Note that for the given input file, the minimum elevation value is 1326, while the maximum elevation
value is 4334. Therefore, the grayscale value for the pixel (0, 0) becomes 102 when scaled from 2537 to
the range [0, 255].
The expected STDOUT output format for the sample input is given below. Note that your output
must match the given output format for full credit.
########## Mission 0 ##########
The most cost-efficient path’s size: 333
The most cost-efficient path has a cost of: 1105.9901586977617
Sometimes there may be a case where it is impossible to reach the destination from the source point.
If we assume the maximum flying height of 2000 instead of 4000 meters, as in our previous example, then
there is no suitable path such that the drone can fly, as the elevations are too high and the drone gets
stuck. In such a case, when there is no possible path, the following output should be printed instead:
########## Mission 0 ##########
ERROR PathNotFound: There is no most cost efficient path that meets all criteria!
3 Mission 1 – Finding the Path with the Least Elevation
In certain scenarios, it can be beneficial to determine the most optimal route for traveling on land. For
example, when traversing mountainous terrain on foot, you may want to identify the path that results in
the least overall change in elevation with each step taken. This can be referred to as the path of least
resistance and can be useful in a variety of applications where efficient land travel is necessary.
3.1 Background Information and Mission Objectives
In situations where there are many options to choose from, a
“greedy” strategy involves selecting the option that appears
to be the best choice in the current moment. An example
of such a path is illustrated on the right. For this mission,
you are expected to apply the following greedy approach to
look for the path that results in the least change in elevation
In the case of our 2D grid map, you can assume that an asset
(e.g., a soldier, a drone, etc.) is positioned at a source point
on the grid and that this asset needs to escape from the grid
towards the East (right). At each step, a possible move is
only into one of the three adjacent cells in the next column.
During this greedy escape, the assumption is that the cell with
an elevation closest to the current cell’s elevation will be chosen as the next step, regardless of whether
this involves moving uphill or downhill. This is illustrated in the scenario examples given below.
The figure above shows a few possible cases for choosing where to take the next step assuming the asset
is at the center point with an elevation of 100 meters. Assume that in the case of a tie, we would always
prefer to just go straight forward, and in the case of a tie between moving NE or SE, we would always
prefer to move towards NE (up instead of down).
Mission Objective: Find and draw the path with the least change in elevation
from the given source point towards the East.
3.2 Input File Format
Your program should use the same grid obtained in the previous mission as the input.
3.3 Expected Solution and Output Format
For the given sample input map file and the source point with coordinates (150, 300), the expected escape
path with the least elevation change towards the East (yellow pixels) is depicted in the figure below.
You are expected to complete:
• getLowestElevationEscapePath(), getLowestElevationEscapePathCost(),
and drawLowestElevationEscapePath() methods of the IMECEPathFinder
The expected STDOUT format for the sample input is given below. Note that your output must
match the given output format for full credit. Also, note that the size of the path means the total
number of points that are visited, including the source and destination.
########## Mission 1 ##########
The size of the escape path with the least elevation cost: 330
The escape path has the least elevation cost of: 12747
Must-Use Starter Codes
You MUST use this starter code. All classes should be placed directly inside your zip archive. Feel free
to create other additional classes if necessary, but they should also be directly inside zip.
Grading Policy
• Submission: 1%
• Implementation of the methods: 90%
– Regular Expressions for Input Data: 10%
– Mission 0: 50%
– Mission 1: 30%
• Output tests: 9%
Important Notes
• Do not miss the deadline: Thursday, 01.06.2023 (23:59:59) .
• Save all your work until the assignment is graded.
• The assignment solution you submit must be your original, individual work. Duplicate or similar
assignments are both going to be considered as cheating.
• You can ask your questions via Piazza (
bbm204), and you are supposed to be aware of everything discussed on Piazza.
• You must test your code via Tur3Bo Grader
(does not count as submission!).
• You must submit your work via with the file hierarchy
given below:
– b<studentID>.zip
∗ <FILE>
∗ <FILE>
∗ <FILE>
∗ <FILE>
∗ <FILE>
• The name of the main class that contains the main method should be You MUST
use this starter code. The main class and all other classes should be placed directly in your zip
archive. Feel free to create other additional classes if necessary, but they should also be inside the
• This file hierarchy must be zipped before submitted (not .rar, only .zip files are supported).
• Usage of any external libraries is forbidden.
• Do not submit any .jar or .class files.
Run Configuration
Your code will be compiled and run as follows:
javac -cp *.jar *.java -d .
java -cp .:* Main input_parameters.dat
Windows Powershell
javac -cp *.jar *.java -d .
java -cp ‘.;*’ Main input_parameters.dat
Windows CMD
javac -cp *.jar *.java -d .
java -cp .;* Main input_parameters.dat
The submissions will be subjected to a similarity check. Any submissions that
fail the similarity check will not be graded and will be reported to the ethics
committee as a case of academic integrity violation, which may result in the
suspension of the involved students.
[1] TÜBTAK Uzay, “İMECE”,, Last Accessed: 17/04/2023.
[2] Wikipedia, “İMECE”,, Last Accessed: 17/04/2023.
[3] Directorate of Communications (DoC), “İMECE satellite launches into space”,
imece-satellite-launches-into-space, Last Accessed: 17/04/2023.
[4] Anadolu Ajansı, “Türkiye to launch new observation satellite on Tuesday”,
turkiye-to-launch-new-observation-satellite-on-tuesday/2867773, Last Accessed: 17/04/2023.
[5] Stanford Nifty Assignments, “Nifty Assignment: Mountain Paths”,, Last
Accessed: 16/05/2023.


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