Professor of School of Engineering, Design and Built Environment, Western Sydney University, Australia. His research interests cover Industry 4.0, Additive Manufacturing, Advanced Engineering Materials and Structures (Metals and Composites), Multi-scale Modelling of Materials and Structures, Metal Forming and Metal Surface Treatment.
2024-02-24
2024-01-04
2023-11-02
Manuscript received March 27, 2023; revised May 6, 2023; accepted June 8, 2023.
Abstract—Automated Storage (AS) are designed to store and retrieve products in specific locations within manufacturing, warehouses, institutions, and others. These AS involve the usage of robots to move the stored items in and out of the warehouse. However, a challenge for AS systems is to solve the path planning for finding shortest path in a minimum amount of time while avoiding collisions with other robots or static obstacles. Fuzzy Logic systems are widely used in several application areas requiring mimicking the human decision logic under uncertainty. In this paper, we proposed an Automated Storage (AS) robot navigation by using three Fuzzy subsystems combined together to ensure path planning with obstacle avoidance. These three fuzzy subsystems are: Reach Target, Avoid Obstacle, and Escape Cul-De-sac. Therefore, fuzzy rules are employed along with the corresponding defuzzication process to control left and right wheel movement steps of the robot. These systems achieve reaching the goal (using the first subsystem) while avoiding different obstacles on the way (using the second subsystem), even the ones that form a trap (using the third subsystem). These three systems will be used for path planning and following. The overall model was simulated using C# code. The initial results showed the effectiveness of the model in different scenarios: namely no obstacles, static ones, traps, and dynamic obstacles. The path length was comparable to that of traditional shortest path methods such as Dijkstra and A*. The results were also compared to a newer method called APSO. The system’s response was quick due to the fewer needed instructions and reduced memory storage needs. All this was done assuming a constant speed for robots and dynamic obstacles. Keywords—automated storage, fuzzy logic, path planning, obstacle avoidance, robotics Cite: Chadi F. Riman and Pierre E. Abi-Char, "Fuzzy Logic Control for Mobile Robot Navigation in Automated Storage," International Journal of Mechanical Engineering and Robotics Research, Vol. 12, No. 5, pp. 313-323, September 2023. Copyright © 2023 by the authors. This is an open access article distributed under the Creative Commons Attribution License (CC BY-NC-ND 4.0), which permits use, distribution and reproduction in any medium, provided that the article is properly cited, the use is non-commercial and no modifications or adaptations are made.
