journal of mathematics research

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Path planning in Unmanned Aerial Vehicles (UAVs): Overview, Challenges,
and Solutions
Hazha Saeed Yahia1, Amin Salih Mohammed2
1
Faculty of Engineering, Department of Information Technology, Duhok Polytechnic University,
Duhok, Iraq, hazha.yahia@hotmail.com
2
Faculty of Engineering, Department of Information Technology, Lebanese French University,
Erbil, Iraq, kakshar@gmail.com
Abstract
Path planning is a significant problem while designing UAV systems. Though a massive amount
of research suggests solutions for the issues and challenges of UAV path planning, the issues and
challenges still exist. On the one hand, UAV path optimization problems involve infinite
variables due to the continuous UAV trajectory to be determined. On the other hand, the issues
are often impacted by various real-world restrictions (such as connection, fuel limits, collision
avoidance, and terrain avoidance), which are challenging to simulate since they change over
time. Besides, path planning selects the shortest optimal path and avoids obstacles and collisions
during the flight. This manuscript investigates the state-of-the-art history, classification, and
applications of UAVs, then the main challenges in UAV design are overviewed. One of the main
challenges in UAVs is path planning with obstacle avoidance. In addition, the paper provides a
comparison and summarization of the leading methodologies and strategies for path planning.
Keywords: Unmanned aerial vehicles, drones, UAV classification, UAV challenges, path
planning.
1. INTRODUCTION
The recent advanced technology in the aerospace industry has dramatically impacted the growth of the
UAV industry and its development over the last decades. The UAVs have high mobility and quick
deployment capabilities; therefore, they can perform tasks in dangerous and hostile environments, not
only in the military but also in civil missions. An unmanned aerial vehicle (UAV) is an aircraft that
flies autonomously or under the pilot's control to deliver lethal or non-lethal payloads [1]. Besides,
UAVs offer new possibilities for different applications at a feasible expense [2]
Although UAVs have undergone massive development over decades and recently have been used in
all areas, UAVs are still facing many challenges, technical limitations, and legal issues. Because of
their ease of use, nowadays, UAVs have a risk to privacy due to their ability to spy on people,
organizations, and governments. Therefore, many governments try to impose proper rules, regulations,
and ethical rules for UAV license and procedures. Law enforcement is trying to make significant
efforts to stop rogue UAVs by signal jamming and attacking and capturing them. Furthermore, as
mentioned previously, many countries are working on developing UAVs, and this requires a need to

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have interpretability between all the different parts of the system. The idea of standard interpretability
was started by the North Atlantic Treaty Organization (NATO) in the early 1990s when NATO
conducted the Interpretability Design Study without forcing countries to follow their design [3].
Although UAVs have different shapes, sizes, weights, operations, different ways for takeoff and
landing, and many other differences, they still the UAVs face many limitations, including regulations,
the data post-processing, posting, tracking, weight, stability, loss of connection, limitation in flight
range, and payload weight that they can carry [3].
The purpose and motivation of this paper are to review the literature about unmanned aerial vehicles,
the history of UAVs, their classification, and their main issues and challenges. Followed by a detailed
review of the path planning challenges in UAVs and the existing methods and techniques for solving
the path planning challenges. Figure 1 shows the structure of this paper and the content of each part
and section.
The rest of the paper, section 2, includes an overview of the UAVs and their history, classifications,
and structure. Section 3 consists of the challenges of UAVs; section 4 includes an overview of the path
planning and the main challenges in UAV path planning. Finally, section 5 consists of the available
solutions for path planning in UAVs in literature, followed by the conclusion.
Figure 1. Structure of the paper
2. OVERVIEW OF UNMANNED AERIAL VEHICLES
The recent advanced technology in robotics has dramatically impacted the UAV industry and its
development over the last decades. Due to the UAV's high mobility and fast deployment capabilities,
the aerospace sector has seen a dynamic growth in UAV use. Remarkably, UAVs can perform tasks in
dangerous and hostile environments, not only in the military but also in civil missions.
Unmanned aerial vehicles (UAVs), sometimes called drones, are powered flying objects that are
operated remotely and autonomously and carry payloads like cameras, sensors, communication
devices, or other payloads [4]. Due to their tiny size and lightweight, the simple operating system
decreased operational hazards, and significant advantages of quick access from one location to
another, UAVs are frequently utilized [5][6]. Additionally, because of their excellent mobility and
ability to take off and land vertically, UAVs have become quite popular in the surveillance industry

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[5]. These features provide them with significant benefits over various platforms or settings. The
following section gives an overview of UAVs' creation, categorization, and use.
2.1 History of UAVs
Despite the fast development of UAVs nowadays and using them in all areas and applications, the idea
behind UAVs initially was to use them as a weapon on the battlefields to target the enemy (another
side)'s sites. There are considerable disputes about the history of UAVs and when the designing and
development of UAVs started. Few studies indicated that the history of UAVs is back to the 1840s
when Austria sent boob-filled balloons to attack Venice in Italy. Other studies suggest that history
goes back to the 1900s. At the same time, others show that the history is back to 1943. Even though all
the above evidence about the UAVs, the UAVs developed during World War II, especially by Nazi
forces in Germany and the department of defense in the USA. Since then, UAV manufacturing had
grown dramatically and entered a new era when UAVs started to be used in civil applications and
missions [7].
The Austrian army released 200 balmy air balloons with explosives during the Austrian siege of
Venice in 1849, expecting the wind would take the balloons. The experiment had only limited success
because of the unpredictability of the weather, but the concept was still viable for future investigation
[8][9]. Moreover, radio-controlled vessels underwent a new stage of development when Nikola Tesla
created the first one (a radio-controlled boat) towards the end of the 19th century. Later, Tesla's
invention was employed during reconnaissance during World Wars I and II[10].
During the WWI participant countries of the war developed considerable eccentric weapons,
especially by Nazi forces and other countries. Aerial torpedoes and flying bombs accounted for many
of these weapons [11]. However, these weapons have problems with crews and difficulty landing and
recovering, with difficulty in stabilization during the flight. For instance, the pilotless aircraft
developed utilizing Archibald Low's radio-controlled methods [9]. Later, in 1917, Britain developed
and brought to the USA a pilotless aircraft known as the "Hewitt-Sperry Automatic Airplane," "Flying
Bomb," or "Aerial Torpedo," but the war ended before using the new aircraft.
Figure 2. Kettering Bug, QH-50C DASH drone, and Black Hornet Nano [8][11]

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After WWI, the British royal navy launched an aircraft with no pilot known as Larynx. Besides,
during the 1930s, many countries tried to develop new UAVs; in 1931, the UK created the "Fairy
Queen UAV," a radio-controlled aircraft. In 1935 the USA army developed RP-1 aircraft, but it was
unsuccessful. Later in 1938, the RP-1 was developed into RP-2 by the UAS army, but again, it was
unsuccessful.
During WWII, using target drones continued for antiair gunnery practice. Also, utilizing radio-
controlled drones and flying glider bombs by the Allies and the Axis to deliver munitions. For
instance, the USS Aaron Ward (DD-483), BG-1 drone, BG-2 drone, TBD Devastator aircraft, SB2C
Helldiver, SB2d Destroyer driver, and other examples.
After the outbreak of WWII and with the beginning of the Cold War, which was fought between the
Soviet Union and its satellite countries and the United States of America and its allies, then a war
between North Korea and South Korea, followed by the Vietnam war, and finally, the Gulf war, many
people lost their lives [11]. During the Vietnam war, the UAVs were successfully used for
reconnaissance and during the wars in Afghanistan and Iraq, the military values of UAVs were
proven. Besides, they evaluated various weapons, especially in the field of UAVs. These wars led
some countries to work more on research for developing the UAV industry [11]. As a result of the
maturation of technology through the 1980s and into the 1990s, the interest of the military sector in
developing UAVs continues to grow[8].
The contemporary quadcopter UAV started to appear as hobby kits in the 1990s and 2000s. The
"Draganflyer quad helicopter," created in 1999, was well-liked by UAV researchers and garnered
notoriety after being used. The first consumer UAV was later released in 2010 under the name "AR
Drone" by the French company Parrot, and it could be flown and controlled over Wi-Fi on mobile
devices. Since then, the UAV business has expanded in both the military and public sectors.
2.2 Classification of UAVs
Due to the fast development in the UAV industry since the 1990s, there are nowadays numerous
UAVs in the market that are different in characteristics, mechanisms, and configurations [12].
Depending on the mission of the UAV, the available UAVs have different specifications, equipment,
the number of rotors, altitude, sizes, ranges, the flight time, altitude, payload, and shapes [13][14].
Still, there is no uniform classification of the UAVs and classified them on different criteria [15].
Therefore, in this paper, the classification of UAVs is based on a variety of factors, including
aerodynamics, landing, size, and mission performance (applications), as shown in Figure 3.

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Figure 3. Classification of UAVs
2.2.1 Based on the aerodynamic
Based on the aerodynamic construction, the UAVs are classified into four categories, fixed wings,
rotor wings, multirotor, and ducted fan UAVs [16]. The fixed-wing UAVs are the type of UAVs that
has good performance for large-scale infrastructures and [17] can carry a heavy payload and are
usually used in missions and operations requiring high-speed, such as in the military, surveillance
operations, and mapping applications [18]. Due to fuel consumption (gas engine powered), fixed-wing
UAVs travel a great distance, with an average flight time of around 16 hours. Compared to other drone
varieties, this UAV can fly at a high altitude and carry greater weight [19][20].
The second type is the rotary-wing UAVs, which carry small payloads and are easy to maneuver, land,
and takeoff [17]. These types are more often used in commercial and for-profit endeavors like aerial
photography and mapping [6][7].
The multirotor UAVs, which have more than one motor and are the third type, are simple to operate,
have flexible mobility, and are adaptable to utilize. A multirotor can fly in all directions, including up,
down, backward, forwards, and sideways [18][21]. Besides, multirotor can take off vertically. Hence,
they have disadvantages in requiring a lot of power, and travel time is short. Widespread applications
for multirotor include mapping, aerial photography, surveillance, inspection, and monitoring.
Multirotor have four types based on the number of motors: tricopter, quadcopter, hexacopter, and
octocopter [18][19]. Typically, trirotor platforms have three rotors linked to their arms. Due to the
minimal number of rotors, these platforms are less stable and have modest lift capacity, despite their
cheap cost.
Quadrotor platforms are by far the most common and have been shown to be versatile, with fewer
moving parts than hexarotor and octorotor platforms. With just four limbs, quadrotors may be built
with a modest diameter, making them suitable for recreational use. Due to its dependability and
subsequent popularity, commercial quadrotor platforms are regarded as the prototypical multirotor
design.

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The last type is the Ducted fan UAV which has the characteristics of vertical takeoff, landing and
maneuvering at different angles. Moreover, they can adapt to complex environments and complete
challenging missions. Due to their characteristics, this type of UAV is preferred to be used for special
missions[22].
Figure 4. Classification of UAVs based on aerodynamic.
2.2.2 Based on the landing
UAVs are classified based on the landing techniques into three categories, VTOL, HTOL, and Hybrid.
Like a traditional helicopter, VTOL UAVs are capable of vertical takeoff, landing, and horizontal
propulsion through the air. This kind is intended to carry out a broad range of commercial and military
applications under challenging circumstances [1]. The VTOL UAVs are either monorotos such as
Orincopter or ducted fans or multirotor such as tricopter and quarcopter. The propulsion systems of
HTOL UAVs are located either on the front or back of the fuselage. UAVs of this kind provide
horizontal takeoff and landing. The HTOL comes in a different form, including fixed-wing and
morphing-wing. While hybrid UAVs combine VTOL and HTOL and provide the advantages of both
with higher performance and efficiency, this kind is still in its infancy [23][24].
2.2.3 Based on the size
UAVs are classified as nano, mini, macro, small, tactical, and large or combat UAVs [25]. Nano, mini,
macro, and small UAVs are made with tiny rotors and motors. They are extremely small and are
simple to operate and fly. Besides, they are fixed wings or multirotor [20]. Most missions using this
kind of UAV include surveys and search and rescue efforts [26][27][28]. Another type is Tactical
UAVs, they are small-size UAVs, around 1.5 meters, and light weights, 1 kg. In the military, this type
of UAV is used for emergency management and critical mission operations. Despite many
advantages, the interest in tactical UAVs has decreased [29]. While the large UAVs are the types of
UAVs that have a big weight, usually more than 150 kg and the range of flight is between 70 to 300
km and in some UAVs (the combat UAVs) is around 1500 km. In general, the large UAVs are the
UAVs that are used in military and for military missions [30].

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Figure 5. Classification of UAVs based on the size.
2.2.4 Based on performing missions (Applications)
Based on missions the UAVs performed, UAVs are classified into military missions and civil missions
[31]. Military applications include detection, identification, search and rescue, emergency responses,
border control, and tactical logistics. At the same time, those civil missions include policing duties,
traffic spotting, fisheries protection, pipeline surveys, sports events, film coverage, agricultural
operations, and many other applications [32]. Due to their simplicity in deployment, low maintenance
costs, excellent mobility, and hovering capabilities, UAVs are utilized in civil applications.
2.3 UAVs hardware designs
Unmanned aerial vehicles (UAVs) are aircraft with directed flight paths and no pilots on board. UAV
is an integrated part of the Unmanned aerial systems (UAS). Unmanned aerial systems (UASs) are
unmanned air vehicles and related equipment that are either remotely piloted or fly autonomously. The
UAS consists of an air vehicle with no pilot on board [4], a control system, control link
(communication link), and payload, as shown in Figure 6. UAVs can make instantaneous decisions
intelligently without human interaction [33][20][34].
The air vehicle is a pilotless UAV that comes in different shapes, sizes, and types, as shown
previously. The air vehicle consists of the fuselage, which is the main body structure of the air vehicle.
The fuselage carries the payload, the main body structures of the UAVs, such as; the UAV flight
controller, the telemetry transmitter, anti-jamming, cameras, sensors, automatic takeoff and landing
system, and many other parts [35]. Unmanned aerial vehicles (UAVs) have a ground station that
manages and controls them, and this ground station is referred to as the control system. Whether the
drone is being directly remotely flown by a person or is designed to fly autonomously, it is a crucial
component of drone operations [36]. The primary method for sending and receiving information
across radio frequencies is the control link. It includes antennas, amplifiers, transmitters, receivers,
power sources, and frequencies [37]. Finally, the payload is the additional sensors, devices, or
armaments carried by the fuselage of the unmanned aerial vehicle (UAV)[35].

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Figure 6. UAS structure
3. CHALLENGES OF UAVS
Even though the UAV industry has recently had a dramatic improvement in both military and civil
sectors, and many countries, companies, and researchers deal with it and its services and application, it
still faces many challenges. These challenges are identified from literature and incident reports such as
path planning, surveillance, navigation, communication, energy efficiency, technical standards,
coordination, air policing, ground services, and other challenges [38]. The difficulties inherent in UAV
path planning are the primary subject of this article.
Path planning in UAVs is their most significant challenge and has recently been studied actively. Path
planning refers to finding an optimal path from start to destination in the most feasible way while
avoiding obstacles and threats to the flying environment with the least possible cost, such as flying
time, fuel consumption, and other charges. Path planning in UAVs involves accomplishing the goal
safely while keeping a velocity more significant than the minimum rate. This means UAVs cannot
follow a path with sharp turns or vertices [13]. Hence, path planning has many challenges and issues
in UAVs, such as movable obstacles, complex environments, finding the shortest path, multi-agent
UAVs, complex map-terrain, and producing smooth trajectories.
Generally, robots need to localize their current location to decide their path; they need to know about
their environment and what is included. Either by having a map of the environment or using sensors to
navigate the environment. Therefore, several challenges are connected to route planning in robots.
These issues include movable obstacles, multi-agent robots, determining the shortest path, complicated
map-terrain, creating smooth trajectories, complex surroundings, and natural motion. While in UAVs,
path planning is the same as in other types of robots with a slight difference in that the velocity must
be minimum and avoid paths with sharp turns or vertices [13]. Besides, path planning will be the key
technology to achieve the autonomous execution of tasks.

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4. SOLUTIONS
The optimization issue of path planning has received significant attention recently. Path planning often
aims to find the best or nearly the best route from the source or starting point to the destination or
distention [39]. The ideal route may be the shortest, avoid obstructions, prevent collisions in hazy
conditions, and complete its objectives [4][40]. UAVs need to localize their present location, generate
a map (if one doesn't already exist), and if the environment is uncertain, it deals with their situation.
They need to avoid obstacles to identify the best route. Additionally, when looking for the best route,
the UAV must take competency, accuracy, and execution time into account.
4.1 Classifications
Despite the current works and research on path planning in UAVs, finding the optimal path is yet a
challenge. Due to the complexity of the problem itself, solutions of this problem vary in literature, and
there are different classifications and views for this. Such as solutions based on the type of path
planning, local or global, or based on the space and environment, static environment or dynamic
environments, and many other bases for classifications. Figure 7 shows the general classification of
solutions available in the literature. Despite all the different categories of classification, all the
categories are related and cannot be separated. For instance, global path planning is offline, and so on.
4.1.1 Based on the type
The first classification is based on the type, which classified into global path planning and local path
planning. When information about the environment and its terrain and obstructions is known, global
path planning creates the path from start to destination. Therefore, the path was completely planned
before the movement [41]. In comparison, in local path planning, the information about the
environment or unknown or partially known, and the UAV depends on the sensors to discover the
environment, and the UAV needs to change the map and the path whenever changes happen [42].
4.1.2 Based on the space
The second classification depends on the space domain, there are two types of spaces: two-
dimensional (2D) and three-dimensional (3D) environment. The 2D is a "flat" environment that has
horizontal and vertical dimensions [43]. The 3D environment, besides the vertical and horizontal
dimensions, has a depth dimension that gives rotation and visualization from multiple perspectives and
planning the path in the 3D environment is a complex multi-objective optimization problem that has
many constraints [44].
4.1.3 Based on the time
Third, based on the time domain includes offline and online time domains and these two types are
very close to local and global path planning. In online mode, the UAV flies in an environment whose
information is partially or entirely unknown. The UAV must explore the environment using sensors
and update its path according to the changes in the environment. The path plan will be in real-time
[45]. While, the offline mode, the agent has complete information about the environment and its
obstacles, plus the full planned path and its starting and destination points [44].

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4.1.4 Based on the methods.
Finally, based on optimization methods, approaches and algorithms, and based on this criteria the path
planning solutions are classified into three main sub categories as, classical methods, map-based
methods, and heuristic and meta-heuristic methods [46]. The first category is the classical methods
that are used for optimizing the path planning in UAVs. Classical methods were popular before the
development of intelligent techniques. Although there were widespread, these methods are not
guaranteed, and the cost of using them is high, and in case of an uncertain environment, they might fail
[47]. The classical methods have different methods and algorithm, in this paper, three methods have
been covered as artificial potential field, cell decomposition, and the road map.
Electrical charges and the electric fields they produce are the primary sources of inspiration for the
APF approach. It operates by combining the forces resulting from their attraction and repulsion
capacity. The UAV and the obstacles often get comparable charges, but the UAV and the target
typically receive different costs. Therefore, the UAV feels attracted to the objective and repulsed by
the barriers. The UAV is navigated across an uncharted area to its target while avoiding obstacles
using the direction of the resulting force [48].
Figure 7. Artificial potential field [49]
In the cell decomposition method, the environment is divided into smaller regions called cells. These
cells are either pure cells that do not contain any obstacles or corrupted cells that contain obstacles.
Based on the cells, a connectivity graph is constructed [47]. The agent uses this connectivity graph to
traverse from the start to the destination, as shown in Figure 8.

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Figure 8. Cell decomposition method [47]
Graph search algorithms determine a path from start to the destination by checking some map nodes to
find the path. Many algorithms exist in this category, such as Depth-First search (DFS), Bredth_first
search (BFS), and Vest-first search algorithms [50].
The second category is the map-based methods that are based on a pre-defined map of the
environment and then a path planned from start to destination. These types of methods are usually the
global path planning methods. There are many algorithms and techniques that are based on the map-
based methods such as Dijkstra algorithm, A* algorithm, Dubins curve, Voronoi Diagram Method,
and many other algorithms. Edsger Dijkstra developed the Dijkstra algorithm in 1959, and it is based
on a weighted graph in that each node has a weight [51]. It was finding the shortest path from start to
destination based on the path's weight. The Dijkstra algorithm visits all the nodes within the graph to
reach the destination, which requires more time and is considered a disadvantage of this algorithm
[50].
Hart introduced A* algorithm in 1968, based on the concepts of the Dijkstra algorithm for finding the
shortest path [52]. This algorithm has many advantages, such as its fast finding of the shortest path and
reasonable real-time performance. A* algorithm uses an occupancy grid map to assign a cost for all
the nodes and traverse the map to find the shortest path based on the lowest cost of the nodes [53][54].
Hence, this algorithm does not guarantee finding the shortest path and is prone to fall in local
optimum, and it is difficult to apply in large environments [55].
The third type is heuristic and meta-heuristic algorithms. Previously the term heuristic was used, and it
refers to techniques for finding the optimum solution among the viable options. The algorithms imitate
natural systems and phenomena and maintain the qualities of parallel operation and information
exchange between agents. But recently the term meta-heuristic used instead of heuristics and meta-
heuristic algorithms are optimization methods and high-level problem independent methods that
mimic the natural behavior for finding the optimum solution. Meta-heuristics are developed to find
solutions that are good enough in an acceptable time [56]. Recently these algorithms are recognized as
efficient methods and have become viable and superior compared to other classical methods, since
these algorithms are applicable to optimization problems in the real world and do not place restrictions
on the formulation of the optimization problem (like requiring constraints or objective functions to be
expressed as linear functions of the decision variables) [57]. Meta-heuristics in general have two types
single-based solutions and population-based solution meta-heuristics. In this paper, four meta-heuristic

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algorithms will be reviewed as the Particle swarm optimization (PSO), Grey wolf optimizer (GWO),
Artificial bee colony (ABC), and the Bat algorithm (BA).
Kennedy and Eberhart developed the particle swarm optimization (PSO) method in 1995, drawing
inspiration from the fish schools and the intelligent swarm behavior of bird folks. The PSO is easy to
implement and has strong robustness [58]. The PSO started with a random set of solutions and then
searched for an optimal solution through iterative updates [59]. The second algorithm is the Grey wolf
optimizer (GWO) that introduced by Seyedali Mirjalili in 2014 that mimics the behavior of the Grey
wolf optimizer (GWO)[60]. The inspiration for this algorithm is the social hierarchy of grey wolves
and hunting mechanism behavior. Grey wolves guided by three wolves, alpha, beta, and the delta, and
these wolves guide other wolves (W) to the best area in searching space. And they hunt large prey in
packs and rely on cooperation among individual wolves.
Figure 9. Grey wolf hierarchy [60]
The Bat algorithm (BA), a meta-heuristic algorithm inspired by nature, was created by Xin-She Yang
in 2010. For distance determination, this algorithm imitates the echolocation of the bat. Bats flit
around at different speeds and directions. Depending on how close their target is, they may
automatically change the frequency (or wavelength) and rate of pulse emission [61].
Figure 10. Bats’ Echolocation behavior [62]
The last example of meta-heuristic algorithm is the Artificial bee colony (ABC) algorithm that was
developed by Dervis Karaboga in 2005. When the population is first created, ABC creates a
population of solutions with a uniform distribution, where each solution is a dimensional vector. The
population's optimization issue for a certain food supply has an inverse relationship with the number of
variables. Based on knowledge from personal experiences and the fitness value of the new solution,
the hired bees change the existing solution. The bee replaces the old food source with the new one and

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discards the old one if the new food source's fitness value exceeds that of the old food source. The
position is updated with the size of steps required to get the new locations using the dimensional
vectors established previously in the first phase [63].
5. CONCLUSIONS
The optimum route planning challenge for UAVs is referred to as UAV path planning. The primary
goal of the optimization path is to locate a safe flight path that uses the least amount of energy to carry
out the UAV mission. For finding the optimal path for UAVs, literature contains many proposed
methods and techniques; these methods are divided into different categories based on various criteria.
Such as based on the type, space, time, and based on methods or algorithms. In general, all the classes
are connected and cannot be separated. The primary purpose is to analyze the factors that affect the
path planning UAVs and find optimal solutions.
This paper reviewed the research progress in this area; despite all the methods and techniques, path
planning still needs to be considered as the main challenge facing the development of UAVs. The fast
growth of technology In the area of robotics and the aviation industry leads to requiring higher
standards of UAVs, and recently UAVs are not using In the military only; UAVs are used now In
many civil applications, and this requires autonomous UAVs with a collision-free path with less
energy, time, and cost.
CONFLICTS OF INTEREST: The authors declare no conflict of interest.
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