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Dr. Hussein Hamdy Hussein Shehata :: Publications:

Mobile Robot Obstacle Avoidance Based on Neural Networks with a Standardization Technique
Authors: Karoline K. Farag, Hussein H. Shehata, and Hesham M. El-Batsh.
Year: 2021
Keywords: Robot; Path Planning; Obstacle Avoidance; Neural Networks; Standardization Technique
Journal: Journal of Robotics
Volume: vol. 2021
Issue: Not Available
Pages: Not Available
Publisher: Hindawi
Local/International: International
Paper Link:
Full paper Hussein Hamdy Hussein Shehata_1129872.pdf
Supplementary materials Not Available

Reactive algorithm in an unknown environment is very useful to deal with dynamic obstacles that may change unexpectedly 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.

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