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A combination of Adaptive Neuro-fuzzy inference system and neural network for mobile robot dynamic obstacle avoidance
Zead Mohammed , Basil Mahmood , Saad Saeed , Aws Hazim
Abstract:   (60 Views)
A mobile robot must be autonomous to avoid obstacles while traveling towards the target. The problem of dynamic obstacle avoidance is still a significant challenge. Reactive mobile robot navigations handled this problem, but using a single-stage module leads to a deficiency and a limitation in performance. This paper proposes combining an adaptive neuro-fuzzy inference system and a neural network. The data for obstacle severity classification were used to train the Bayesian regularization Back-Propagation Neural Network. The relative velocity and distance between the mobile robot and obstacles determine the zone. Zone 1 is dangerous, and Zone 5 is safe. This paper uses an Adaptive Neuro-Fuzzy Inference System (ANFIS) to avoid obstacles during the mobile robot's motion and to avoid collision. Based on our empirical study, we consider three essential features in this paper: the relative speed, distance, and angle between the robot and the obstacle as inputs to the obstacle avoidance system ANFIS. The output was a suggested steering angle and speed for the mobile robot. The simulation results for the tested cases show the capability of the proposed controller to avoid static and dynamic obstacles in a fully known environment. Our results show that the Adaptive Neuro-Fuzzy Inference System enhances the proposed controller's performance, reducing path length, processing time, and the number of iterations compared.
 
Keywords: : mobile robot navigation, dynamic obstacle avoidance, Neural network, Adaptive Neuro-Fuzzy Inference System, path planning.
Full-Text [PDF 3030 kb]   (15 Downloads)    
Type of Study: Research Paper | Subject: Deep Learning
Received: 2025/03/24 | Revised: 2025/12/07 | Accepted: 2025/10/22
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Creative Commons License This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Creative Commons License
© 2022 by the authors. Licensee IUST, Tehran, Iran. This is an open access journal distributed under the terms and conditions of the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) license.