Design and Development of an Autonomous Mobile Robot for Unstructured Indoor Environments

This research work presents the design and the development of a cost-effective autonomous mobile robot for locating misplaced objects within unstructured indoor environments. The tools integrated into the proposed system for perception and localization are a hardware architecture equipped with LiDAR, an inertial measurement unit (IMU), and wheel encoders. The system also includes an ROS2-based software stack enabling autonomous navigation via the NAV2 framework and Adaptive Monte Carlo Localization (AMCL). For real-time object detection, a lightweight YOLO11n model is developed and implemented on a Raspberry Pi 4 to enable the robot to identify common household items. The robot’s motion control is achieved by a fuzzy logic-enhanced PID controller that dynamically modifies gain values based on navigation conditions. Remote supervision, task management, and real-time status monitoring are provided by a user-friendly Flutter-based mobile application. Simulations and real-world experiments demonstrate the robustness, modularity, and responsiveness of the robot in dynamic environments. This robot achieves a 3 cm localization error and a 95% task execution success rate.