Autonomous Mobile Robots (AMR) use algorithms and electronic components like sensors and actuators to determine their location (in real-time), chart a course, and move around (while avoiding obstacles). This project focuses on implementing Simultaneous Localization and Mapping (SLAM) and A* Path-Planning on a commercially available prototyping robot, the TurtleBot3 Burger, to autonomously navigate through a predefined, 4×4 rectangular maze. Equipped with a 360° Light Detection and Ranging (LiDAR) sensor and open-source code, the TurtleBot generates an occupancy grid map for navigation. The project enhances the default coding by integrating the A* algorithm to optimise pathfinding from a starting point to a goal while accounting for obstacles. Beyond this, future code versions may incorporate a Dynamic Window Approach (DWA) to improve real-time obstacle avoidance. The TurtleBot features an OpenCR board and runs on a Raspberry Pi 4 with Ubuntu 22.04.5 LTS and ROS2 Humble. This project aims to improve SLAM-based localisation accuracy and efficient real-time path planning for autonomous robots in complex environments. The results contribute to advancing robotic applications in search and rescue, industrial automation, and other fields.

https://linktr.ee/resqbot

https://github.com/ROBOTIS-GIT/turtlebot3