UAV PATH PLANNING USING GENETIC ALGORITHMWITH PARALLEL IMPLEMENTATION

International Journal of Computational Science and Information Technology (IJCSITY)
Vol. 10, No. 2, May 2022
DOI : 10.5121/ijcsity.2022.10201 1
UAV PATH PLANNING USING GENETIC
ALGORITHMWITH PARALLEL IMPLEMENTATION
Mohammad AlRaslan and Ahmad Hilal AlKurdi
Faculty of Informatics Engineering, Idleb University, Idleb, Syria
ABSTRACT
The objective of path planning algorithms is to find the optimal path from a source position to a target
position. This paper proposes a real-time path planner for UAVs based on the genetic algorithm. The
proposed approach does not identify any specific points outside or between obstacles to solve the problems
of the invisible path. In addition, this approach uses no additional steps in the genetic algorithm to handle
the problems resulting from generating points inside the obstacles, or the intersection between path
segments with obstacles. For these reasons, this paper introduces a simple evaluation method that takes
into account the intersections between the path segments and obstacles to find a collision-free and near to
optimal path. This evaluation method take into account overlapped and intersected obstacles. The sequential
implementation for all of the genetic algorithm steps is detailed. This paper explores the Parallel Genetic
Algorithms (PGA) models and introduces the parallel implementation of the proposed path planner on
multi-core processors using OpenMP. The execution time of the proposed parallel implementation is
reduced compared to sequential execution.
KEYWORDS
Path planning, UAV, Genetic algorithm, Parallel genetic algorithm, OpenMP, Speed up.
1. INTRODUCTION
In recent years, Unmanned Aerial Vehicle (UAV) and Unmanned Ground Vehicle (UGV) Path
planningalgorithms become important research topics. These algorithms should find an optimal
path between start and target positions. There are two main types of path planning algorithms:
global path planning (offline path planning) and local path planning (online path planning) [1–3].
Many studies have detailedand classified path planning algorithms into several categories [4, 5].
Genetic Algorithm (GA) is one of the widely used algorithms for path planning [3]. Many studies
implemented GA for UAV path planning and tried to give a collision-free path, where all of the
path segments don’t intersect with any obstacle on the map. To avoid an obstacle on the map,
many studies suggest to identify specific pointsoutside or between obstacles as crossing points.
These points are either randomly generated or at specific locations around the obstacle. Randomly
generated points may not be outside the obstacle, and points around obstacles may not give a
collision-free path, especially if the obstacles are close to each other or intersect with each other.
Other researchers used additional steps in GA to solve the problems resulting from generating
points inside the obstacles, or the intersection between the path segments withobstacles.
In [6], the Escape operator was introduced in GA for the waypoints that are generated inside the
sensing area of radar (obstacle). The solution was to move these waypoints to other positions
away from the radar sensing area to get a collision-free path. This means repeating random
generation of new waypoints, with condition (if point outside all of obstacles, then …), to reach
free waypoint outside obstacles. An Escape method is also presented in [7], with Simulated

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Annealing algorithm. The corner points of a square that covers the threatening radar area
(obstacle) are used as crossing points to avoid obstacles. Authors in [8] gave improved GA for
path planning. They presented three kinds of path planning schemes: Directly through, Insertion
through, and Bypass through. When the path intersects with the threatening region (obstacle),
these schemes choose a free path by determining specific pointsfor crossing (mid-point between
two threatened zone, or a vertex of threatened zone). The improved GA in [8], has been modified
in [9]. Likewise, the authors relied on that the path is feasible when all the guidance points are
feasible. An avoiding technique was proposed in [10], by adding eight virtual points as crossing
points. These points are placed in the orbit of the radar with a 45° angle. Simulated Annealing
algorithm is used in [11], and two methods are introduced to give a good optimal solution that at
the same time avoids circular radar threats (2-Opt method and two nodes random swapping
method). The 2-Opt method stops at a point where it cannot improve the path because of the radar
areas.The resulted path was not optimal in some of the cases when the number of radar threats is
high (6-8). Authors in [12] used GA and presented a Forbidden Zone avoidance technique. The
Forbidden Zone’s shape is assumed as a square type. This technique relies on covering the
Forbidden Zones with the Guard Zones. When the path is invisible, specific points are used (one
or two Guard Zones corner points) and added to the path. The researcher mention that this
technique works when there is no overlapbetween the Forbidden Zones.
Furthermore, most current path planning studies did not stop at just presenting the algorithm
model; they also provided a parallel model for this algorithm to achieve real-time path planning.
This paper proposes an approach for real-time UAV path planning. This approach deals with all
points in search space as a crossable point. We discuss all GA steps and introduce a simple
evaluation methodto find a collision-free and near to optimal path. This method takes into account
the intersection states between the path segments and the obstacles. In addition, it does not
specify any points on the map to solve the problems of the invisible path. The evaluation method
deals with intersected or overlapped obstacles. After that, we detail how we achieved parallelism
on multi-core systems. We present the Parallel Genetic Algorithms (PGA) and its implementation
models and discuss implementation details of this approach on multicore architecture using
OpenMP. We compare the sequential time with the parallel time and introduce the speed up.
The remainder of this paper is organized as follows: Section 2 introduces GA principles and
contains the proposed sequential approach for path planning. Section 3 deals with parallel
computing, OpenMP, and parallel genetic algorithm (PGA). Section 4 presents the proposed
parallel implementation. Section 5 focuses on simulation and experimental results. Finally, we
conclude the paper in Section 6.
2. PROPOSED SEQUENTIAL APPROACH
This section introduces all proposed GA steps for path planning. GA is usually started with a
populationof individuals (chromosomes). Each individual is made up of a set of genes. The first
task in GA is to figure out how to describe the environment, represent individuals, and calculate
fitness. Following that,GA begins to develop solutions utilizing the following steps: crossover,
mutation, and selection. Pseudo-code 1, describes the GA steps in our code. These steps are
repeated until satisfying a condition,which is the number of iterations.
2.1. Environment modelling and chromosome encoding
We propose the environment as a 2-D map, with several circular obstacles. These obstacles may
represent a radar sensing range, enemy weapon zones, or difficult terrain. Figure 1, shows a

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sample ofthe map where each obstacle is defined by its center (x,y) that represents the position,
radius r represents the range, and a crossing factor (f) that relates to the nature and type of the
obstacle. As we will see later, the crossing factor (f) allows adding different types of obstacles to
the map. The individual is a path that connects the start and target points through a series of
interconnected points (genes). The startpoint is at the beginning of each path, and the target point
is at the end, as shown in Figure 1.
The number of interconnected points along each path is the same. The POPULATION_SIZE
parameter determines the number of paths (individuals) within the population, whereas the
PATH_SIZE parameterdetermines the number of points within every path. This means that each
path, as shown in Figure 2, has (PATH_SIZE -1) line-segments. In the map, every point along
any path (except the start and targetpoints) is produced at random.
Pseudo-code 1. Proposed sequential approach
srand(time(NULL));int iter = 0; initializeSettings(); initializeMap();
numSelected = (int)(POPULATION_SIZE * SELECTION_RATE); numCrossed =
(int)(POPULATION_SIZE * CROSSOVER_RATE);
numMutated = POPULATION_SIZE - numSelected - numCrossed;
GenetateInitialPoputation();while (true)
{
Evaluation();Selection();
if (iter >= MAX_ITERATIONS)
break;Crossover(); Mutation(); iter++;
}
Figure 1. Map sample
As seen in Figure 1, the random points may be located inside an obstacle. This means that many
line- segments along the path have the potential to cut through one or more obstacles (but at
a highercost). The same thing happens when two generated points are outside the obstacle, but the
line-segmentconnecting them cuts through it.

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Figure 2. Population and individual representation
Table 1 gives the parameters of the genetic algorithm and the map. All of these parameters are
initializedat the beginning of the algorithm.
Table 1. The parameters of GA and the map
parameter significance
POPULATION_SIZE the number of individuals (paths) in population
PATH_SIZE the number of the points in every path
MAX_ITERATIONS the maximum number of repetitions in GA while loop
SELECTION_RATE selection rate
CROSSOVER_RATE crossover rate
MUTATION_RATE mutation rate
MaxX the maximum value on the X-axis
MaxY the maximum value on the Y-axis
source the source point
target the target point
obstacles[] an array of obstacles
2.2. Generating initial population
The first step in GA is to generate the initial population. Paths within the initial population are
createdat random. As illustrated in Figure 2, we need to generate (PATH_SIZE-2) random points
for each pathbetween start and target points (𝑃1 → 𝑃PATH_SIZE − 2 ).
Pseudo-code 2. Generating initial population
void GenetateInitialPoputation()
{
for (int i = 0; i < POPULATION_SIZE; i++) {
myPop[i].path[0] = myMap->source; for (int j = 1; j < PATH_SIZE - 1; j++)
{
myPop[i].path[j] = getRandomPoint();
}
myPop[i].path[PATH_SIZE - 1] = myMap->dist;
}
}
Generating a random point is achieved by generating its coordinate (x,y) on the map. These

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coordinates are specified in the [0, MaxX] and [0,MaxY] ranges on X-axis and Y-axis,
respectively. The rand() andsrand() functions in the C++ language can be used to generate random
numbers. Pseudo-code 2 includesthe generating initial population step.
2.3. Evaluation
As mentioned earlier, path points are generated at random inside the map. This means that some
pointscan be placed within the obstacles. In addition, two neighbouring points can be outside the
obstacle, while the line-segment connecting them cuts through the obstacle. In this study, and
depending on the previous assumptions, the fitness function evaluates the path in two aspects:
basic cost and crossing cost, as illustrated in Eqn. 1 and Eqn. 2.
𝑡𝑜𝑡𝑎𝑙 𝑐𝑜𝑠𝑡(𝑓𝑖𝑡𝑛𝑒𝑠𝑠) = 𝑏𝑎𝑠𝑖𝑐 𝑐𝑜𝑠𝑡 + 𝑐𝑟𝑜𝑠𝑠𝑖𝑛𝑔 𝑐𝑜𝑠𝑡 (1)
The basic cost is the path’s length. To put it another way, the path's length is the sum of the
lengths of the line-segments that make it up. Euclidean distance is used to calculate the
distance between two points. The crossing cost is calculated for line-segments within the path
that travelled across one or more obstacles. The crossing cost relates to the crossing distance
within the obstacle and a factor f. Thisfactor relates to the nature and type of the obstacle, where
there is a possibility to add different type ofobstacles (in crossing cost) to the map, and to deal
with overlapped or intersected obstacles.
𝑛−1 𝑚−1
𝐹𝑖𝑡𝑛𝑒𝑠𝑠(𝑝𝑎𝑡ℎ𝑘) = ∑ ( 𝐷𝑖𝑠𝑡𝑎𝑛𝑐𝑒(𝑃𝑖, 𝑃𝑖+1) + ∑ 𝑓𝑗 ∗ 𝐶𝑟𝑜𝑠𝑖𝑛𝑔𝐶𝑜𝑠𝑡(𝑃𝑖, 𝑃𝑖+1, 𝑂𝑗))
𝑖=0 𝑗=0
(2)
Where:
 n: is the number of line-segments in each path (individual).
 m: is the number of obstacles on the map.
 𝑓𝑖𝑡𝑛𝑒𝑠𝑠(𝑝𝑎𝑡ℎ𝑘): denotes the total cost (fitness) of the Kth
path, which represents the Kth
individual in the population.
 𝐷𝑖𝑠𝑡𝑎𝑛𝑐𝑒(𝑃𝑖, 𝑃𝑖+1): is the Euclidian distance between the two points 𝑃𝑖, 𝑃𝑖+1 within the
path.
 𝐶𝑟𝑜𝑠𝑖𝑛𝑔𝐶𝑜𝑠𝑡(𝑃𝑖, 𝑃𝑖+1, 𝑂𝑗): refers to the crossing cost when line-segment (𝑃𝑖, 𝑃𝑖+1)
crossed through the obstacle (𝑂𝑗).
 𝑓𝑗: is a factor that depends on the type of obstacle 𝑂𝑗 and the issue at hand Figure 3 explains
the intersection states between a line-segment and the obstacle that is represented bya circle.
Calculating the crossing distance has many cases based on these states. In the first state,
Figure3 (a), there are two intersection points 𝐼1, 𝐼2. The crossing cost is the distance from 𝐼1
to 𝐼2 multiplied by the factor f. Based on Eqn. 2, the overall crossing cost for the line-
segment (𝑃1,𝑃2) is calculated foreach obstacle 𝑂𝑗.

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Figure 3. Line-segment and Circle intersection states
To find out the distance crossed by the line-segment within the obstacle, we must find the
intersection points between the straight line and the obstacle (the circle).
The equation of the line passing through two points 𝑃1(𝑥1, 𝑦1),𝑃2(𝑥2,𝑦2) is defined in Eqn. 3.
𝑦2 − 𝑦1
𝑦 − 𝑦1 =
𝑥 − 𝑥
(𝑥 − 𝑥1)
2 1
(3)
The equation of a circle is given in terms of its center (𝑥0, 𝑦0) and radius 𝑟 as in Eqn. 4.
(𝑥 − 𝑥0)2 + (𝑦 − 𝑦0)2 = 𝑟2 (4)
By solving the two equations, Eqn. 3 and Eqn. 4, we get three situations, as shown in Figure 4:
 Delta>0: there are two solutions (two distinct intersection points) and the straight line
cutsthrough the circle.
 Delta=0: there is one solution (intersection point) and the straight line is tangent to circle.
 Delta<0: there isn’t any solution and the straight line lies completely outside the circle.
Figure 4. Straight line and Circle states
The approach code contains a function that discusses the first situation between the straight line
and thecircle to find out the coordinates of two intersection points.

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The previous discussion determines the intersection states between the straight line, that passes
throughtwo points (the ends of the line-segment 𝑃1, 𝑃2), and the circle. We cared about the third
situation when the straight line cuts the obstacle at two points (delta>0). Now, an additional
discussion will clarify theintersection states between the line-segment and the circle, as illustrated
in Figure 3. In the following, we introduce two concepts:
 Valid Intersection point: The intersection point 𝐼 between the straight line and the circle is
saidto be a valid intersection point between the line-segment 𝑃1(𝑥1, 𝑦1) → 𝑃2(𝑥2, 𝑦2) and
the circle if its coordinates (on X-axis and Y-axis) lies between the two ends of the line-
segment(𝑃1,𝑃2). This concept is illustrated in Pseudo-code 3.
 𝑃 𝑖𝑛𝑠𝑖𝑑𝑒 𝑂𝑏𝑠𝑡𝑎𝑐𝑙𝑒 𝑂𝑗: A point P lies inside the obstacle 𝑂𝑗 if the distance between the point
Pand the center of the obstacle 𝑂𝑗 is less than the radius: 𝑑𝑖𝑠𝑡𝑎𝑛𝑐𝑒 (𝑃𝑗,(𝑂𝑗.𝑥, 𝑂𝑗.𝑦)) < 𝑂𝑗. 𝑟
Pseudo-code 3. Check if the Intersection point is valid
if (((p1.x <= I.x <= p2.x) OR(p2.x <= I.x <= p1.x)) AND ((p1.y <= I.y <= p2.y) OR(p2.y <= I.y <=
p1.y)))
return true;
else
return false;
Depending on the previous discussion, we get a set of cases to calculate the crossing cost when the
line-segment (𝑃1,𝑃2) crosses through the obstacle 𝑂𝑗, see Table 2 and Figure 3.
Table 2. Cases of line-segment and circle crossing cost
𝑷𝟏 𝒊𝒏𝒔𝒊𝒅𝒆 𝑶𝒋 𝑷𝟐 𝒊𝒏𝒔𝒊𝒅𝒆 𝑶𝒋 𝑰𝟏 𝒊𝒔 𝒗𝒂𝒍𝒊𝒅 𝑰𝟐 𝒊𝒔 𝒗𝒂𝒍𝒊𝒅 Crossing cost
true false true false 𝑓𝑗 ∗ 𝑑𝑖𝑠𝑡𝑎𝑛𝑐𝑒(𝑃1,𝐼1)
true false false true 𝑓𝑗 ∗ 𝑑𝑖𝑠𝑡𝑎𝑛𝑐𝑒(𝑃1,𝐼2)
false true true false 𝑓𝑗 ∗ 𝑑𝑖𝑠𝑡𝑎𝑛𝑐𝑒(𝑃2,𝐼1)
false true false true 𝑓𝑗 ∗ 𝑑𝑖𝑠𝑡𝑎𝑛𝑐𝑒(𝑃2,𝐼2)
true true x x 𝑓𝑗 ∗ 𝑑𝑖𝑠𝑡𝑎𝑛𝑐𝑒(𝑃1,𝑃2)
false false true true 𝑓𝑗 ∗ 𝑑𝑖𝑠𝑡𝑎𝑛𝑐𝑒(𝐼1,𝐼2)
Pseudo-code 4 shows how to evaluate paths in the population. For each obstacle 𝑂𝑗 on the map,
we assign a real value to the factor 𝑓𝑗. The value of 𝑓𝑗 can be computed from several
coefficients, 𝑓𝑗 = 𝑔(𝑐1, 𝑐2, … . 𝑐𝑛), depending on the obstacle type and the studied issue, such as
riskiness level of the obstacle. When the path passes through several intersecting or overlapping
obstacles, the crossing costfor each obstacle will be calculated separately.

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Pseudo-code 4. Evaluation step
void Evaluation()
{
for (int i = 0; i < POPULATION_SIZE; i++)
{
for (int j = 0; j < PATH_SIZE - 1; j++)
{
BasicCostOnePath+=EuclideanDistance (myPop[i].path[j], myPop[i].path[j + 1]);
CrossingCostOnePath+=CrossingCostInAllObstacles(myPop[i].path[j], myPop[i].path[j +
1]);
}
TotalCostOnePath = BasicCostOnePath + CrossingCostOnePath;myPop[i].Fitness =
TotalCostOnePath; BasicCostOnePath=TotalCostOnePath=CrossingCostOnePath=0;
}
}
2.4. Selection
The selection step's goal is to choose the best individuals for the next generation. In this work, the
rankselection method is employed. Firstly, all individuals are sorted in descending order, with the
fitness value as the sort key. Then they are selected (saved) to the new population. (SELECTION
RATE*POPULATION SIZE) determines the number of selected paths. The sorting algorithm has
an impact on GA's execution time. MergeSort, which has a time complexity of 𝑂(𝑛 log 𝑛), is
employed inthe sequential implementation.
2.5. Crossover
The crossover process combines two paths into a new child path. A single-point crossover is
employed. A random number is generated to indicate a position in the parent’s path (from 1 to
PATH_SIZE–3). Path points are exchanged between these parents according to this position to
generate a child’s path. The two paths for crossover operation are chosen at random from the
selected paths. During the experiments, the same parent is likely to be chosen twice for the same
crossover process. Therefore, we adopted the production of a single child from the crossover
process to reduce the frequency of identicalindividuals within the new population. The crossover
probability is defined by the CROSSOVER_RATE parameter, and the number of child paths
resulting from crossover is defined by(CROSSOVER_RATE * POPULATION_SIZE). Pseudo-
code 5, describes the crossover step in the proposed approach.
Pseudo-code 5. Crossover step
void Crossover()
{
for (int i = 0; i < numCrossed; i++)
{
int r1 = random_num(0, numSelected - 1); int r2 = random_num(0, numSelected - 1);
index = i + numSelected;
myPop[index] = CrosserInOnePosition(myPop[r1], myPop[r2]);
}
}

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2.6. Mutation
The mutation step helps in increasing the population diversity. The mutation operation on a path
is performed by choosing a random position in this path (from 1 to PATH_SIZE - 2). Then,
replace the point in this position with a randomly generated point. The random path for mutation
operation is chosenfrom selected and crossed paths. The mutation probability is defined by the
MUTATION_RATE parameter, and the number of mutated paths is defined by
(MUTATION_RATE *POPULATION_SIZE). Pseudo-code 6, describes the mutation step in the
proposed approach.
Pseudo-code 6. Mutation step
void Mutation()
{
int index;
for (int i = 0; i < numMutated; i++)
{
int r = random_num(0, numSelected + numCrossed - 1);index = i + numSelected +
numCrossed;
myPop[index] = mutatIndividual(myPop[r]);
}
}
3. PARALLEL GENETIC ALGORITHM
The simultaneous usage of many compute resources to solve a computational problem is known
as parallel computing. Flynn's taxonomy was refined to divide computer system architectures into
shared- memory systems, distributed-memory systems, and hybrid systems based on memory
structure [13, 14].In recent years, various tools, such as OpenMP, have been available to help the
programmer in converting sequential algorithms into parallel.
OpenMP stands for open multiprocessing. It is a standard application programming interface (API)
thatprovides a portable, scalable model for shared memory parallel applications [13–15]. Fortran
and C/C++ have both implemented this standard. OpenMP is made up of a set of compilers
directives, function libraries, and environment variables. It works based on a fork-join concept. A
program in the OpenMP environment has sequential and parallel regions. The master thread starts
executing sequentially after launching the program until the compiler encounters a parallel region
directive. Then,a team of threads is created, and start execution simultaneously.
As explained previously, the GA should be implemented in a parallel style to achieve real-time
path planning and overcome the GA's computational drawback. Many studies, such as [16–18],
have discussed the Parallel Genetic Algorithm (PGA). These researches focus on models,
hardware architectures, APIs, and middleware used to implement PGAs. There are three main
types of PGA: Master-slave, Fine-grained, and Coarse-grained [18, 19]. In the master-slave
model, there is a single population, and all GA steps are applied sequentially, except for the
evaluation step that is performed in parallel. Many studies have detailed implementing PGA in
shared memory multi-core computers. In [20], they implemented their solution on a multi-core
system using MATLAB. The PGA is also implemented for Design Space Exploration using
OpenMP, as in [21]. Other researches present how tospeed up GA using OpenMP on multicore
processors [22, 23]. Java concurrent programming has beenused in many researches to implement
GA in multicore parallelism [24, 25].

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4. PROPOSED PARALLEL IMPLEMENTATION
Because GA is an iterative process, we focus on using OpenMP to parallelize its loop. In GA, the
generating initial population step is executed only once at the beginning of the algorithm. In each
loopiteration, the other GA steps are executed consecutively. The master-slave PGA model uses a
single population (but the evaluation step is distributed among threads). This model is the most
suited for shared memory architectures because it provides global access for all threads to
the population’s individuals stored in the shared memory. The proposed approach focuses on
distributing not only the evaluation step (in the master-slave PGA model) but also all genetic steps
between all threads. Pseudo-code 7 illustrates how the parallel implementation of the GA’s steps
will be organized.
Pseudo-code 7. Parallel region organization, threads synchronization, and loop termination
//initialization
//NUM_THREADS is the number of threads participating in work#pragma omp parallel
{
id: // thread index in thread team
srand(time(NULL)*id); //initializing each thread with different seed
// parallel generate Initial Populationwhile (true)
{
// parallel Evaluation (calculate Fitness)
// parallel selection (Bitonic Sort)#pragma omp barrier
if (iter >= MAX_ITERATIONS)
break;
// parallel crossover
// parallel mutation #pragma omp master
{
iter++;
}
#pragma omp barrier
}
}
The while loop is put inside the parallel region (#pragma omp parallel). Setting the parallel region
inside the while loop is inconvenient because it causes the threads to split at each iteration,
resulting in a largeoverhead. Initialization of parameters takes place outside the parallel region,
while the initial populationgeneration takes place inside it.
If the "#pragma omp for" directive is used before each genetic step, all threads will participate.
The work distribution must be as balanced as possible by the schedule clause. Dynamic
scheduling is used in the parallel evaluation step, as each thread will encounter a different path in
terms of intersections. In this case, the work will be less uniform between threads. Parallel
implementation for generating initial population, crossover, and mutation steps is applied using
static scheduling. Threads work in these cases is regular. Some synchronization is required to
make sure that there are no race conditions, and all threads exit the loop correctly. The master
thread is responsible for incrementing iterations in a master construct. This guarantees that all
threads in the team will break when they reach a pre-specifiedMAX_ITERATIONS value.
Bitonic sort is used in the proposed parallel implementation. Bitonic sort does 𝑂(𝑛2 log 𝑛)
comparisons.The number of comparisons is more than Merge Sort, which performs 𝑂(𝑛 log 𝑛),
but Bitonic is more preferable for parallel implementation. It compares elements in a predefined

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sequence and the sequence of comparison does not depend on data. As a result, it takes less
computation time in parallel implementation compared to sequential [26].
5. SIMULATION AND EXPERIMENTAL RESULTS
In this section, simulation and experimental results are introduced in three scenarios. For some
parameters, the value will be the same across all experiments (SELECTION_RATE=0.4,
CROSSOVER_RATE=0.5, and MUTATION_RATE=0.1). The crossing factor (𝑓𝑗) is
assigned a different value for each obstacle. These values fall within the range [4.5-9.5].
The simulation isperformed on a PC with a 2.2GHz Intel Core i7-G8 processor.
Figure 5. Map sample
5.1. Scenario1
The goal of this scenario is to test the algorithm's ability to find a collision-free path that avoids
all obstacles on the map. We find out the number of iterations needed by the algorithm to get this
path. Wedon't care about the path cost in this scenario. GA repeats its steps in a loop. In each
iteration, we test the best path within the population that the algorithm has found in the current
iteration. However, we test the intersection of this best path with all obstacles on the map. If this
path is collision-free and does not intersect with any obstacle, we exit the loop and record the
current iteration at which we exited. Scenario1 was tested on the map that is displayed in Figure
5. Figure 6 shows the number of iterations required by the proposed approaches to reach a
collision-free path for PATH_SIZE=10 and different values of POPULATION_SIZE.

Vol. 10, No. 2, May 2022
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Figure 6. Number of iterations needed to reach a collision-free path when PATH_SIZE=10
Through the simulation, it is clear that the proposed approach tends towards avoiding the obstacles
aftera few iterations. As a result, we do not require any additional steps in the genetic algorithm
to handle the paths that cross through obstacles. It was found through experiment, that when an
obstacle is assigned a high value to the crossing factor, the algorithm regresses to avoid this
obstacle quickly.
5.2. Scenario2
This scenario tests the algorithm's ability to reach the shortest collision-free path. For this, several
experiments are performed on the map that is shown in Figure 5. In Each experiment, the path is
obtained using the proposed approach, and the cost of this path is recorded. After that, the relative
errorbetween the obtained path cost and the optimal path cost is calculated using Eqn. 5.
(5)
Figure 7 shows the relative error for a different number of iterations. Figure 8 shows the obtained
pathafter executing the senario2 simulation for MAX_ITERATIONS =1500.
Figure 7. The relative error between the obtained path cost and the optimal path cost

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13
Figure 8. The obtained path after executing the senario2 simulation for PATH_SIZE=8,
POPULATION_SIZE =512, and MAX_ITERATIONS =1500
Through the simulation experiments, the proposed approach provides a path as close to the
optimum aspossible, even if the path requires passing in a narrow area between obstacles or on an
obstacle arc. This approach does not depend on specific points on the map, (mid-point between
two obstacles, or obstaclevertices), to be crossable points.
5.3. Scenario3
This scenario compares the execution times of sequential and parallel algorithms. The algorithm
is tested on the map that is shown in Figure 5, where there are twelve obstacles. This means much
calculations and execution time are needed to evaluate paths. Table 3 shows the execution time of
the sequential and the parallel algorithms for MAX_ITERATIONS=500, PATH_SIZE=8.
(6)
Table 3. Execution time of the sequential and parallel algorithm when PATH_SIZE=8 and
MAX_ITERATIONS=500
NUM_THREADS POPULATION_SIZE Sequential Time (sec) Parallel Time (sec) Speedup
2
128 2.1436 1.1208 1.914316
256 4.298 2.24 1.919127
512 8.6662 4.518 1.918337
1024 17.4936 9.2766 1.885892
2048 35.9558 18.2686 1.968242
4
128 2.1588 0.634 3.40573
256 4.305 1.275 3.377573
512 8.5526 2.6464 3.232167
1024 17.5414 5.504 3.187353
2048 36.1944 11.2194 3.226128
8
128 2.1748 0.3946 5.512776
256 4.3194 0.83 5.205919
512 8.743 1.7548 4.98314
1024 18.5074 3.983 4.645983
2048 36.326 8.2354 4.41128

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Figure 9. Speedup achieved by parallel implementation
Through the simulation, it clearly appears that the parallel implementation achieves a reasonable
acceleration relative to the number of threads.
6. CONCLUSION AND FUTURE WORK
In This paper, we proposed a real-time path planner for UAVs based on genetic algorithm. An
evaluation method was introduced here to reach the best path that avoids obstacles. All of the
genetic stages for path planning were given in details. Three scenarios of simulation experiments
were presented to prove that this approach tends towards providing a collision-free path that is
close to optimum. Parallel implementation for each step was detailed using OpenMP, and the
speedup was achieved to get fast path planning.
For future works, the circular constructions could be used to smooth the final path. In addition,
for parallel implementation, GPGPU will get more speedup.
ACKNOWLEDGEMENT
The authors would like to thank everyone who have extended his support for successful
completion ofthis work.
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