Mobile Robot Obstacle Avoidance Based on Neural Network with a Standardization Technique

Author:

Farag Karoline Kamil A.12ORCID,Shehata Hussein Hamdy34ORCID,El-Batsh Hesham M.25ORCID

Affiliation:

1. Mechatronics Engineering Department, Alexandria Higher Institute of Engineering and Technology (AIET), Alexandria, Egypt

2. Mechanical Engineering, Faculty of Engineering, Benha University, Benha, Egypt

3. Benha Faculty of Engineering, Benha University, Benha, Egypt

4. Mechatronics Systems Engineering Department, Faculty of Engineering, MSA University, 6th of October City, Egypt

5. High Institute of Engineering and Technology, Mahala El-Kobra, Egypt

Abstract

Reactive algorithm in an unknown environment is very useful to deal with dynamic obstacles that may change unexpectantly and quickly because the workspace is dynamic in real-life applications, and this work is focusing on the dynamic and unknown environment by online updating data in each step toward a specific goal; sensing and avoiding the obstacles coming across its way toward the target by training to take the corrective action for every possible offset is one of the most challenging problems in the field of robotics. This problem is solved by proposing an Artificial Intelligence System (AIS), which works on the behaviour of Intelligent Autonomous Vehicles (IAVs) like humans in recognition, learning, decision making, and action. First, the use of the AIS and some navigation methods based on Artificial Neural Networks (ANNs) to training datasets provided high Mean Square Error (MSE) from training on MATLAB Simulink tool. Standardization techniques were used to improve the performance of results from the training network on MATLAB Simulink. When it comes to knowledge-based systems, ANNs can be well adapted in an appropriate form. The adaption is related to the learning capacity since the network can consider and respond to new constraints and data related to the external environment.

Publisher

Hindawi Limited

Subject

General Computer Science,Control and Systems Engineering

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