Citation: Zhang, R.; Li, S.; Ding, Y.;
Qin, X.; Xia, Q. UAV Path Planning
Algorithm Based on Improved Harris
Hawks Optimization. Sensors 2022,
22, 5232. https://doi.org/10.3390/
s22145232
Academic Editor: Gregor Klancar
Received: 1 June 2022
Accepted: 11 July 2022
Published: 13 July 2022
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Article
UAV Path Planning Algorithm Based on Improved Harris
Hawks Optimization
Ran Zhang
1,2
, Sen Li
1,2,
*, Yuanming Ding
2
, Xutong Qin
1,2
and Qingyu Xia
1,2
1
School of Information Engineering, Dalian University, Dalian 116622, China; nancy444@163.com (R.Z.);
q1533664997@163.com (X.Q.); xiaqingyu0315@163.com (Q.X.)
2
Communication and Network Laboratory, Dalian University, Dalian 116622, China;
dingyuanming@dlu.edu.cn
* Correspondence: leeson1028@163.com
Abstract:
In the Unmanned Aerial Vehicle (UAV) system, finding a flight planning path with low
cost and fast search speed is an important problem. However, in the complex three-dimensional
(3D) flight environment, the planning effect of many algorithms is not ideal. In order to improve its
performance, this paper proposes a UAV path planning algorithm based on improved Harris Hawks
Optimization (HHO). A 3D mission space model and a flight path cost function are first established
to transform the path planning problem into a multidimensional function optimization problem.
HHO is then improved for path planning, where the Cauchy mutation strategy and adaptive weight
are introduced in the exploration process in order to increase the population diversity, expand the
search space and improve the search ability. In addition, in order to reduce the possibility of falling
into local extremum, the Sine-cosine Algorithm (SCA) is used and its oscillation characteristics are
considered to gradually converge to the optimal solution. The simulation results show that the
proposed algorithm has high optimization accuracy, convergence speed and robustness, and it can
generate a more optimized path planning result for UAVs.
Keywords:
flight path planning; Harris Hawks optimization; Cauchy mutation strategy; adaptive
weight; sine-cosine algorithm; unmanned aerial vehicle system
1. Introduction
With the rapid development of the communication technology, sensors, artificial
intelligence and 5G technology, the Unmanned Aerial Vehicle (UAV) plays a crucial role
in modern military war [
1
]. UAV path planning is a key problem in UAV systems [
2
],
and the quality of UAV path directly determines the success or failure of combat missions.
Therefore, it is of great significance to study UAV path planning algorithms in complex
combat environment.
Many experts and researchers have performed in-depth studies on UAV path planning.
According to the dimension of the planning space, it is mainly divided into two-dimensional
(2D) [
3
] and three-dimensional (3D) path planning [
4
]. The developed model for 3D path
planning is stereoscopic, and considers topography and threat factors, which is closer to
the actual environment. However, it increases the complexity of path planning. The UAV
3D path planning algorithms mainly include classical algorithms and swarm intelligence
algorithms [
5
]. The classical algorithms include the A-Star algorithm [
6
], Differential
Evolution (DE) [
7
], Dijkstra algorithm [
8
] and simulated annealing [
9
]. Although these
algorithms have their own advantages, they all have some disadvantages, such as the long
search time and large memory consumption. The swarm intelligence algorithm [
10
] forms
a self-organizing and adaptive stochastic optimization algorithm with bionic behavior by
observing the living habits, foraging behaviors and social characteristics of the biological
populations. Common swarm intelligence algorithms for path planning include the Particle
Swarm Optimization (PSO) [
11
], Firefly Algorithm (FA) [
12
], Ant Colony optimization
Sensors 2022, 22, 5232. https://doi.org/10.3390/s22145232 https://www.mdpi.com/journal/sensors
