This works presents an in-depth performance comparison of path planning algorithms for autonomous mobile robot navigation under simulated and real environment. Five different navigation strategies were systematically contrasted: four planner combinations of A * and RRT* with DWA, TEB and an end-to-end (E2E) policy trained from scratch based on the Proximal Policy Optimization algorithm. The evaluation was performed on a home-made robot with differential drive kinematics, an RP LIDAR A1M8 laser scanner, an Arduino based module, and a Raspberry Pi 4 in indoor warehouse like scenarios. The experimental procedure consisted of several test situations using three different target locations in static and dynamic obstacle environments. Each configuration was executed three times for robustness, which is crucial in the context of probability-based RRT. Performance was assessed using multiple metrics including the number of calls to global planner representing replanning rates, time to reach goal, and for RRT – failed attempts at path generation. Experiments were done in simulation with Gazebo and the results was verified on the real robot. Simulation results indicated that A*-based combinations counted less replanning calls in comparison with RRT-based, and A+TEB, realized the best calculation time when operating in static environments. In dynamic obstacle environment, local planner decision played a more important role than the global planner selection. RRT had a find failure rate of 15% at the end of their used computation time, and A* found paths in case such exist. The exploratory analysis using deep reinforcement learning also demonstrated good performance but computing resources were demanding. System level validation showed reasonable real-world transfer of simulation results to the physical system, albeit with different absolute performance indicators due to sensor noise and wheel slip. The results give practical advice for choosing an algorithm depending on the application requirements, for instance on the complexity of the environment and computational resources available or desired level of reliability. The primary contribution of this paper is to make available empirical data that help practitioners select navigation algorithms suitable for various settings in mobile robot applications.