Autonomous mobile robots are becoming a key part of everyday operations in industries like manufacturing, logistics, healthcare, and even home assistance. A core requirement for these robots is the ability to navigate efficiently and reliably within their operating environments. To do this automation, the robot needs to understand its surroundings, figure out where it is on a map, and find a safe path from where it is to where it needs to go without bumping into anything. This paper presents an effective grid-based path planning solution for autonomous indoor navigation with a mobile robot. Achieving reliable and collision-free navigation in changing environments is a major challenge for mobile robotics. This is especially true when obstacles can appear unexpectedly, requiring quick re-planning. To tackle this issue, an improved A* algorithm was implemented to work closely with LiDAR for environmental awareness. The improved algorithm was added to the robot’s navigation system, and LiDAR data were used for simultaneous localization and mapping (SLAM) with Gmapping. A key improvement was integrating with ROS move_base control instead of using direct velocity control, enabling smoother motion and better path tracking. Additionally, the improved A* path is simplified into a series of crucial waypoints, which are followed by move_base while the system watches LiDAR data in real time to spot obstacles. When a moving obstacle is detected, the planner recalculates the path and updates waypoints, enabling the robot to go around the obstruction and continue toward its goal safely. Tests in real indoor environments showed that the proposed system performs reliably at avoiding dynamic obstacles, navigating smoothly, and achieving goals. By combining heuristic planning, LiDAR perception, and ROS navigation tools, the proposed system offers a practical solution for autonomous mobile robot navigation.