Research Article
An Improved Heuristic Algorithm for UCAV Path Planning
Kun Zhang,
1
Peipei Liu,
1
Weiren Kong,
1
Jie Zou,
2
and Min Liu
2
1
School of Electronics and Information, Northwestern Polytechnical University, Xian, Shaanxi 710072, China
2
Science and Technology on Electro-Optic Control Laboratory, Luoyang, Henan 471009, China
Correspondence should be addressed to Kun Zhang; kunnpu@gmail.com
Received 29 December 2016; Accepted 23 February 2017; Published 10 April 2017
Academic
Editor: Maoguo Gong
Copyright © Kun Zhang et al. is is an open access article distributed under the Creative Commons Attribution License,
which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
e study of unmanned combat aerial vehicle (UCAV) path planning is increasingly important in military and civil eld. is
paper presents a new mathematical model and an improved heuristic algorithm based on Sparse
∗
Search (SAS) for UCAV path
planning problem. In this paper, ight constrained conditions will be considered to meet the ight restrictions and task demands.
With three simulations, the impacts of the model on the algorithms will be investigated, and the eectiveness and the advantages
of the model and algorithm will be validated.
1. Introduction
Nowadays, unmanned combat aerial vehicle (UCAV) has
long been a challenging area for researchers in military and
civil eld. Path planning is dened as looking for the optimal
path of moving objects from the start point to the target
point under specic constraints (including environmental
constraint and movement constraint) []. e path planning
aims to nd the path with the highest survival rate, lower
loss,andshorterperiodoftime.Currently,thepathplanning
problem has been widely used in dierent areas such as
cruise missile, helicopter, and UCAV. In modern warfare,
with the development of various air defense technologies,
there is no doubt that the path planning problem is more
andmorebeingpaidattentiontoinmilitaryeld.Numerous
scholars study the path planning problem constantly. e
represented techniques of UCAV path planning are like PSO
[, ], dynamic planning [],
∗
algorithm [, ], ant colony
algorithm [], genetic algorithm [, ], and so on [–].
Reference [] discussed sparse algorithm, another eective
way, which greatly improves the eciency of the search, but
it is easy to fall into a death cycle under the conditions
of lacking maneuver ability. Reference [] adopted sparse
algorithm, but angle heuristic function is not taken into
consideration and the number of constraint conditions is
small; the simulation result is not scientic. Reference [, ]
proposed the algorithms with large amount of calculation,
making the algorithms unsuitable to seek optimization solu-
tion. To address these problems, an improved heuristic
algorithm [] is studied which takes angle information into
account in a certain range, two methods of trajectory smooth
straightening processing are adopted and compared, and the
corresponding simulations are given in this paper.
2. Related Works
2.1. Basic Mathematical Model. e path planning problem of
UCAV can be modeled as a constrained optimization prob-
lem. Before searching track, ight condition and elements
(like terrain, threats, climate, etc.) of relevant path planning
are represented as symbol information.
Let (
𝐿
,
𝐿
,
𝐿
) be longitude, latitude, and height of a
certainpointinstatespace.epathplanningspacecanbe
represented as a set: {(
𝐿
,
𝐿
,
𝐿
)|0
𝐿
max
𝐿
, 0
𝐿
max
𝐿
,0
𝐿
max
𝐿
}, which represents a space
district. In practical planning, the planning space is divided
into two-dimensional grids or three-dimensional grids; a
series of nodes are acquired and built into a network graph,
as shown in Figure . e path planning problem can be
simply attributed to a combinational optimization problem
for getting the shortest path of the network graph. at is to
say, when UCAV is ying along the path formed by some
Hindawi
Journal of Optimization
Volume 2017, Article ID 8936164, 7 pages
https://doi.org/10.1155/2017/8936164