Abstract:
In this work , intelligent fuzzy controller for mobile robot in various (known and unknown) environments
is build to control of mobile robot. A successful way of structuring the navigation problem treat with the issues
of individual behavior design and action coordination.
The inputs to the proposed fuzzy control method consist of a heading angle between a robot and a specified
target and the distances between the robot and the obstacles to the left, front, and right to its locations, being
acquired by sensors. The output of controller are the speeds of right and left wheels. Simulation results verified
the effectiveness of the controller.
Key words : Mobile Robot , Environment ,Fuzzy Logic , Obstacle avoidance,.
الخلاصة:
في هذا العمل مسيطر مضبب ذكي للروبوت المتحرك في مختلف البيئات (معروفة و غير معروفة) يتم بناؤه للسيطرة على الروبوت
المتحرك.الطريقة الناجحة لتشكيل مسألة القيادة مرتبطة مع مسائل تصميم سلوك مستقل وتنسيق الفعاليات.
المدخلات لطريقة المسيطر المضبب المفترض تتكون من الزاوية الرأسية بين الروبوت والهدف المحدد والمسافات بين الروبوت و
العوائق إلى مواقع اليسار ، اليمين و الأمام للروبوت ، المكتسبة بواسطة المتحسسات .المخرجات للمسيطر هي سرع العجلات اليمنى واليسرى
.نتائج المحاكاة تؤكد فعالية هذه المسيطر .
الكلمات المفتاحية: الروبوت، البيئة، المنطق الضبابي، تجنب العوائق.
1 Introduction
Current developments of mobile robot have attracted the attention of researchers in
the areas of engineering, computer science, mining and others. This is due to the high
potential of mobile robots application. Autonomous mobile robots are robots which can
perform desired tasks in unstructured environments without continuous human guidance
[Frank and Goswami, 2004;Ibrahim and Fernandes, 2004];[ Kim and Cho, 2006];[Waterman,
1989]. Many kinds of robots are autonomous.
A fully autonomous robot in the real world has the ability to:
• Gain information about the environment.
• Travel from one point to another point, without human navigation assistance.
• Avoid situations that are harmful to people.
• Repair itself without outside assistance.
A robot may also be able to learn autonomously. Autonomous learning includes the
ability to:
• Learn or gain new capabilities without outside assistance.
• Adjust strategies based on the surroundings.
• Adapt to surroundings without outside assistance.
Navigation for mobile robots can be well-defined in mathematical (geometrical)
terms. It also involved many distinct sensory inputs and computational processes. Elementary
decisions like turn left, turn right, run or stop are made on the basis of thousands of incoming
signals [David, 1990; Gallistel, 1990; Parhi, 2005]. Navigation is traditionally defined as the
process of determining and maintaining a trajectory to a goal location [Gallistel, 1990].
Humans have uncertain and imprecise information. The main advantages of a fuzzy
navigation strategy lie in the ability to extract heuristic rules from human experience, and to
obviate the need for an analytical model of the process [Seraji and Howard, 2002 ].
In this search, Fuzzy Logic System (FLS) is used to produce the control inputs for the
robot with inputs from various sensors. Sensor signals are fed to the FLS, and the output
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provides motor control commands (e.g., turn right or left). The FLS learns the full
dynamics of the mobile robot online. Fuzzy controller for mobile robot has three inputs and
two outputs. Both inputs and output have three membership functions. Each membership
function is considered as triangular membership function. This search provides a Fuzzy logic
framework to be implemented in the mobile robot for behavior design and coordination.