Version 1
: Received: 2 July 2024 / Approved: 4 July 2024 / Online: 4 July 2024 (10:55:48 CEST)
How to cite:
Stavrinidis, S.; Zacharia, P. An ANFIS-Based Strategy for Autonomous Robot Collision-Free Navigation in Dynamic Environments. Preprints2024, 2024070439. https://doi.org/10.20944/preprints202407.0439.v1
Stavrinidis, S.; Zacharia, P. An ANFIS-Based Strategy for Autonomous Robot Collision-Free Navigation in Dynamic Environments. Preprints 2024, 2024070439. https://doi.org/10.20944/preprints202407.0439.v1
Stavrinidis, S.; Zacharia, P. An ANFIS-Based Strategy for Autonomous Robot Collision-Free Navigation in Dynamic Environments. Preprints2024, 2024070439. https://doi.org/10.20944/preprints202407.0439.v1
APA Style
Stavrinidis, S., & Zacharia, P. (2024). An ANFIS-Based Strategy for Autonomous Robot Collision-Free Navigation in Dynamic Environments. Preprints. https://doi.org/10.20944/preprints202407.0439.v1
Chicago/Turabian Style
Stavrinidis, S. and Paraskevi Zacharia. 2024 "An ANFIS-Based Strategy for Autonomous Robot Collision-Free Navigation in Dynamic Environments" Preprints. https://doi.org/10.20944/preprints202407.0439.v1
Abstract
Autonomous navigation in dynamic environments poses a significant challenge in the field of ro-botics with the primary goals of ensuring both smooth and safe movement. This study introduces control strategy based on an Adaptive Neuro-Fuzzy Inference System (ANFIS) to enhance au-tonomous robot navigation in dynamic environments, emphasizing on collision-free path plan-ning. This strategy employs, a path planning technique to develop a trajectory that allows the ro-bot to navigate smoothly while avoiding collisions with both static and dynamic obstacles. The developed control system incorporates four ANFIS controllers: two are tasked with guiding the robot toward its end point, and the other two are activated for obstacle avoidance. The experi-mental setup conducted in CoppeliaSim involves a mobile robot equipped with ultrasonic sensors navigating in an environment with static and dynamic obstacles. Simulation experiments are conducted to demonstrate the model's capability in ensuring collision-free navigation, employing a path planning algorithm to ascertain the shortest route to the target destination. The simulation results highlight the superiority of the ANFIS-based approach over conventional methods, partic-ularly in terms of computational efficiency and navigational smoothness.
Keywords
mobile robot; navigation; sensors; path planning; static and dynamic obstacles; neuro-fuzzy con-trollers; CoppeliaSim
Subject
Engineering, Mechanical Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
