Aerospace and Electronic Systems Magazine September 2016 - 44


An Advanced Sense and Collision Avoidance Strategy for Unmanned Aerial Vehicles in Landing Phase

(15)

(16)
where k is the number of species in habitat, and E and I are the
maximum emigration rate and immigration rate, respectively. For
research simplicity, it is assumed that E = I = 1, then one can yield:
(17)
The BBO method consists of two main functions: migration
(including emigration and immigration) and mutation.

Figure 5.

Simplified migration model of a habitat.

2) differential flatness-based approach within the UAV dynamic
constraints to generate a feasible reference trajectory. When the
environment is represented as a grid-based model, the solution of
the collision avoidance problem can be thereby considered as selecting a set of waypoints for the UAV to minimize the flight distance and maximize the safe distance with intruders. The optimal
path directing the UAV from the initial position to the pre-arranged
destination is produced based on dynamic migration and mutation.
A differential flatness approach is then applied such that the physical constraints of the UAV can be respected while the generated
trajectory can be feasible.

MIGRATION
The evolutionary procedure of migration is to arrange each solution Hi by immigrating and emigrating features between solutions.
Each Hi has its own immigration and emigration rates. A good
solution has low l and high m, while a poor solution has the opposite characters. After initializing a random set of solutions H =
[H1, H2, ..., Hn], a solution Hi is selected to be modified with the
probability that is proportional to the immigration rate l. To share
features between solutions, the emigrating solution Hj is selected
with the probability that is proportional to the emigration rate m. A
randomly selected SIV in solution Hj is chosen to migrate into Hi.
Migration can be consequently expressed as:

BIOGEOGRAPHY-BASED OPTIMIZATION (BBO)

(18)

BBO is a novel population-based optimization algorithm [27]. The
basic idea of BBO is influenced by the geographical distribution
of species over time and space. Similar to other artificial heuristic algorithms, the search agent in BBO is called a habitat. Each
habitat is assigned a vector of habitants, representing a possible
solution for the proposed problem. The fitness and suitability of
a habitat are defined by the Habitat Suitability Index (HSI). The
feature of the characterized habitability is called the Suitability Index Variable (SIV), which is equivalent to a gene in GA. In this
study, SIVs represent a sequence of discrete waypoints. A solution
with high HSI is better than one with low HSI. On the other hand,
high HSI habitats with large amounts of species are more likely to
emigrate to the low HSI habitats, while habitats with low HSI tend
to immigrate species from high HSI habitats. Motivated by this
natural biogeography, sound solutions tend to share their features
with poor solutions, and poor solutions are more likely to accept
features from good solutions to increase its quality. The movement of species between different habitats is determined by the
immigration rate (l) and the emigration rate (m), formulated as the
functions of the number of species in the habitat. As illustrated in
Figure 5, l is inversely proportional to the number of species and
m is proportional to the number of species, which are both limited
from 0 to 1. Smax is the largest number of species that a habitat can
keep. The equation for emigration rate (mk) and immigration rate
(lk) can be defined as:

44

The emigration operator is used to modify non-elite habitat.
The elitism operation is employed to preserve the elite habitat produced in the previous generation by setting immigration rate l to
zero. In such a way, the best solution can be prevented from being
replaced after immigration. The detailed procedure of migration is
shown in Figure 16 in the Appendix.

MUTATION
Mutation is an event that a given SIV in a given solution is replaced
by a randomly selected SIV based on a mutation rate. The mutation
rate is derived by the function of the species count probabilities.
The equation of the probabilities of each species is written as:

(19)

The mutation rate of each solution, inversely proportional to
the solution probability, is formed as:

IEEE A&E SYSTEMS MAGAZINE

(20)

SEPTEMBER 2016



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