Elbouhy, Seifalla MohamedSeifalla MohamedElbouhyAdamanov, AsanAsanAdamanovBraun, Philipp MaximilianPhilipp MaximilianBraunRose, Hendrik WilhelmHendrik WilhelmRose2025-06-032025-06-032025-04IEEE International Conference on Simulation, Modeling, and Programming for Autonomous Robots, SIMPAR 2025979-8-3315-1685-7https://hdl.handle.net/11420/55770Local path planners are essential for real-time obstacle avoidance, particularly in unpredictable industrial environments. This study compares three ROS 2 local path-planning algorithms: Dynamic Window Approach (DWB), Model Predictive Path Integral (MPPI), and Regulated Pure Pursuit (RPP). These planners are part of the Robot Operating System 2 (ROS 2) navigation stack (Nav2), with DWB and MPPI as built-in controllers, and RPP integrated as a controller option.While previous studies have focused on simulations or simple real-world environments with static obstacles, few have examined dynamic and unpredictable scenarios. To address this, experiments were conducted with a Clearpath Jackal robot in environments with static and dynamic obstacles. Key metrics, including distance to obstacles, frequency of recovery behaviors, and path smoothness, were used to compare the planners performance in both real-world and simulated settings.MPPI offers a strong balance across all metrics, making it ideal for dynamic environments requiring both safety and efficiency. DWB excels in fast navigation, though with closer proximity to obstacles, while RPP produces smooth paths but struggles in highly dynamic environments. These results provide valuable insights for selecting the right local path planner for autonomous robots in industrial applications like manufacturing and warehousing.enDWB | Local Planner | MPPI | ROS2 | RPPTechnology::600: TechnologyConference Paper10.1109/SIMPAR62925.2025.10979015Conference Paper