Citation: Al-Mallah, M.; Ali, M.;
Al-Khawaldeh, M. Obstacles
Avoidance for Mobile Robot Using
Type-2 Fuzzy Logic Controller.
Robotics 2022, 11, 130. https://
doi.org/10.3390/robotics11060130
Received: 27 September 2022
Accepted: 8 November 2022
Published: 16 November 2022
Publisher’s Note: MDPI stays neutral
with regard to jurisdictional claims in
published maps and institutional affil-
iations.
Copyright: © 2022 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and
conditions of the Creative Commons
Attribution (CC BY) license (https://
creativecommons.org/licenses/by/
4.0/).
Article
Obstacles Avoidance for Mobile Robot Using Type-2 Fuzzy
Logic Controller
Mohammad Al-Mallah
1
, Mohammad Ali
2
and Mustafa Al-Khawaldeh
1,
*
1
Department of Mechatronics Engineering, Philadelphia University, P.O. Box 1, Amman 19392, Jordan
2
Department of Electrical Engineering, Philadelphia University, P.O. Box 1, Amman 19392, Jordan
* Correspondence: malkhawaldeh@philadelphia.edu.jo
Abstract:
Intelligent mobile robots need to deal with different kinds of uncertainties in order to
perform their tasks, such as tracking predefined paths and avoiding static and dynamic obstacles
until reaching their destination. In this research, a Robotino
®
from Festo Company was used to reach
a predefined target in different scenarios, autonomously, in a static and dynamic environment. A
Type-2 fuzzy logic controller was used to guide and help Robotino
®
reach its predefined destination
safely. The Robotino
®
collects data from the environment. The rules of the Type-2 fuzzy logic
controller were built from human experience. They controlled the Robotino
®
movement, guiding it
toward its goal by controlling its linear and angular velocities, preventing it from colliding obstacles at
the same time, as well. The Takagi–Sugeno–Kang (TSK) algorithm was implemented. Real-time and
simulation experimental results showed the capability and effectiveness of the proposed controller,
especially in dealing with uncertainty problems.
Keywords:
mobile robot; Robotino
®
; static and dynamic obstacle-avoidance environment; Type-2
fuzzy logic controller; wireless sensor network
1. Introduction
Nowadays, robots are an inseparable part of our life. Robots with movement ability
impose themselves through many applications, including medical facilities, hospitality,
entertainment, package delivery, space, and military. Recently, mobile robots have been
a controlling contributor to human development and one of the fastest growth fields of
scientific research. They have displayed their abilities in helping and substituting humans
in many applications with high efficiency [1].
The obstacle avoidance is an important feature in mobile robots that enables them to
reach their destination point collision free. This necessitates providing them with a decision-
making capability for planning their path autonomously and reacting to the hazards that
may hinder their movements. However, this is no longer easily achieved by using classical
control approaches without prior information available about the environment and using
intelligent control [
1
]. Furthermore, some of the intelligent control methods cannot handle
the high level of uncertainties of sensors, actuators, and environment [2].
For achieving autonomous obstacle avoidance, numerous control strategies have been
developed, among which is the Type-2 fuzzy logic control. Fuzzy logic control is considered
the most vastly used technique for designing controllers that manage suitable performance
in many real-world applications [
3
]. For example, it was used to design a controller
capable of introducing a safe Robotino
®
and tracking its predefined target, as in Ref. [
4
].
A fuzzy logic controller with 153 fuzzy rules was utilized for controlling the Robotino
®
path-tracking issue, while another fuzzy logic controller with 27 fuzzy rules was applied
for the Robotino
®
obstacle-avoidance feature, using the Sugeno fuzzy algorithm. Many
real-time experiments reflected good abilities of the proposed controllers. Moreover, an
autonomous mobile robot was designed and implemented by using a fuzzy logic controller,
Robotics 2022, 11, 130. https://doi.org/10.3390/robotics11060130 https://www.mdpi.com/journal/robotics
