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You are here: Home ❯ PARTICLE SWARM OPTIMIZATION (PSO) MODEL FOR ROBOT NAVIGATION

** Format: MS WORD Chapters: 1-5**

** Pages: 66 Attributes: STANDARD RESEARCH **

** Amount: 3,000**

**CHAPTER ONE**

**INTRODUCTION**

**1.0** **BACKGROUND OF THE STUDY**

Mobile robots have been successfully applied in many areas
such as medical and military applications, ocean and space exploration, sports
and entertainment, security, public and domestic duties e.t.c of which their
environments can either be static or dynamic. They can perform difficult and
hazardous tasks with complex requirements and often have to do so autonomously,
without the aid of a human operator. Path planning is a very important task for
the autonomous mobile robot. It is necessary to ensure a collision-free motion
in an obstacle-prone environment in order to navigate safely from the start
state to the goal state. Normally, there are various feasible paths for a robot
to reach the target from the start location, but in circumstance, the best
feasible path is selected according to some guideline such as shortest
distance, smoothness of the path, minimum energy consumption etc. or the most
adopted criteria is the shortest distance with the minimal possible time.
Collision-free path planning plays an important role in mobile robots
navigation and is often a fundamental requirement for proper task execution.
This complex task poses many difficulties: computational complexity, adaptation
to changing environment and determining a reasonable evaluation function for
the generated path.

Mobile robots path planning research field
commenced in the middle of 1960s. At the beginning, researchers worked on
static environments and used statistical and mathematical methods such as
Artificial Potential Field and Visibility Graph to solve the problem. Many
efforts have been conducted in robotics research for solving the fundamental
problem of motion planning, which consists of generating a collision-free path
between start and goal positions for a robot in a static and completely known
environment, where there could be obstacles. Mobile robot motion planning in
dynamic environments has been studied extensively in. On-line planning
algorithms are needed for changing environments, but these typically suffer
from a lack of generality in the knowledge of the space in which they are
executed. Lots of studies exist on the motion planning for robotic systems
using various approaches. There is strong evidence that a *complete* planner (i.e. one that finds a path whenever one exists and reports
that no one exists otherwise) will take time exponential in the number of
degrees of freedom (dof) of the robot, and that the algorithm belongs to a
class of problem known as **NPC**omplete.

Recently some classical approaches, such as cell
decomposition, potential field method, road map and sub goal network have been
presented in the field of mobile robotics. In a cell decomposition method a
two-dimensional map is divided into several grids and the path is created in
them. Another case of a classical approach is a potential field method in which
the controlled robot is attracted by the destination while simultaneously being
repelled by the obstacles. These path planning algorithms suffer from some
drawbacks, e.g., a solution may not be optimal because the algorithm gets stuck
in local minima or a new solution has to be generated again when the
environment changes and therefore the original path can become infeasible. As a
result, many heuristic based methods, such as fuzzy logic, artificial neural
network, nature inspired algorithms and hybrid algorithms were created. These
methods can overcome drawbacks of the classical ones, but they do not guarantee
to find the best solution. Still, the result can be sufficiently close to the
optimal one.

Particle Swarm Optimization (PSO) is a metaheuristic
algorithm which is inspired by the social foraging behaviour of some animals
such as bird flocking and fish schooling. It was developed by Kennedy and
Eberhart in 1995. In Particle Swarm Optimization (PSO), a problem space is
covered with an initial population of solutions in which they are guided to
search for the optimum over a number of generations. The concept of PSO is that
each particle randomly searches as through the problem space by updating itself
with its own memory and the social information gatherers from other particles.
An attractive feature of PSO is that its implementation is simple and effective
and if the path exist this algorithm can find it. Movement of a robot position
is realized by the Particle Swarm Optimization algorithm. PSO convergence to
the best solution by adjusting the trail of each individual particle toward its
best location based on the best of itself and global best on the neighbour
particles. The modification of a robot position is realized by position and
velocity information.

**1.1 MOTIVATION**

The common
problems that motivates researchers in
the field of autonomous robot navigation is the ability to ensure these robots move safely and fast from
its start state to its goal state avoiding any obstacle along its way
especially in an unknown environment and also producing an optimal result.
Examples of implementation of autonomous robot navigation algorithm includes
automobile industry (self-driving cars), unmanned spacecraft e.t.c.

The
limitation of the research work carried out by Memon *et al*. (2015) and Bilbeisi *et al*. (2015) are the key motivation for this research
work. These include;

The
inability to generate safe path for mobile robot from the start state to the
goal state in an unknown environment Memon *et al*. (2015) and also navigating in an unknown environment needs online presence
which is the drawback of the research work of Bilbeisi *et al*. (2015).**
**

**1.2 PROJECT OBJECTIVES**

The specific objectives of this project are to:

a)
Design a Particle Swarm Optimization (PSO) based model for
mobile robot path planning.

b)
simulate the result of the developed model and;

c)
evaluate the performance of the model.

**1.3 METHODOLOGY**

In order to achieve the objectives stated above, the below
listed methods will be implemented for this research work:

a)
Previous path planning algorithm structure will be
reviewed.

b)
A PSO based model will be developed to plan the path
for the mobile robots.

c)
The PSO based model is implemented to determine the
optimal route of a robot from source to destination point until any obstacle is
detected on its path. The obstacle-avoidance decisions made for the robot is
only on past and current obstacle motion data obtained by the vision system.
Once any obstacle is detected over the optimized path, the obstacle avoidance
is done by moving robot towards the nearest safe point around the obstacles
boundary.

d)
The population is first initialized with random positions,
velocity, target and epsilon. Then, the system evaluates the fitness of each
particle using equation (1) which is the distance of robot from target position
in a particular direction. If any particle’s current fitness is smaller than
previous fitness in any axis, then this system saves the current position of
the particle as P_{best}, otherwise recall previous best position of particle,
where particle achieve less distance from the target. After calculation of
personal best values of all particles, compare all particle’s best positions (P_{best})
and select one particle that has best position among all and point out that
location as Global best (G_{best}). Then, PSO algorithm is applied for
finding the next velocity and location of each particle using equation (2) and
(3), respectively. Large values of velocity may results in wide change of
particle’s position which causes the divergence problems and particle may leave
the search space. Therefore, to handle these situations, inertia weight and
velocity clamping techniques are used to reduce the effect of previous velocity
on the next velocity. The formula for Inertia weight “ω” is represented by
equation (4).

Fitness
Function = |Target − Current Location|....................... (1)

vij
t+1 = [vij t × ωt+1] + [c1 × r1j t × [Pbest ,i t − xij t ]]+ [c2 × r2j t ×
[Gbest − xij t ]]..............(2)

xij
t+1 = xij t + vij t+1 .............................................(3)

ωt+1
= ωmax − ωmax −ωmin Total Number of Iterations
× Current Iteration........................................(4)

**1.4 CONTRIBUTION
TO KNOWLEDGE**

At the end of this project, an efficient Particle Swarm Optimization (PSO)
model for robot navigation would have been developed.

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