Abstract:
We present a comprehensive approach for mobile robot navigation under the Robot Operating
System (ROS) Noetic, utilizing a Z-number-based fuzzy logic framework. The proposed
methodology addresses the challenges of uncertainty and imprecise information in robot
navigation by employing Z-numbers, which extend the capabilities of traditional fuzzy numbers.
The system incorporates various implemented methods to enable robust navigation in complex
environments. Firstly, line trajectory generation facilitates efficient straight-path following, while
circular trajectory generation enables smooth and precise circular motion. To assess the
performance of the proposed method, extensive simulations were conducted using the TurtleBot3
robot in a Gazebo simulator within the ROS Noetic framework. Mapping techniques are utilized
to build an accurate representation of the environment, allowing the robot to perceive its
surroundings effectively. The robot's navigation strategy uses LiDAR sensors and the Adaptive
Monte Carlo Localization algorithm with particle filter mapping to provide real-time mapping and
localization. Path planning is accomplished through local and global planners, generating shortterm and long-term navigation plans. The local planner guides the robot around local obstacles,
while the worldwide planner charts a course toward the robot's destination, considering the overall
environment. Obstacle avoidance is implemented to generate collision-free paths by dynamically
detecting and circumventing obstacles in the robot's vicinity. SLAM techniques simultaneously
localize and map, creating an accurate robot environment map. Z-number-based fuzzy logic
approach under ROS Noetic provides reliable robot navigation performance in diverse scenarios,
advancing autonomous mobile robot capabilities in complex environments.
