Cadete, TomásPinto, Vítor H.Lima, JoséGonçalves, GilCosta, Paulo Gomes da2025-02-102025-02-102024Cadete, Tomás; Pinto, Vítor H.; Lima, José; Gonçalves, Gil; Costa, Paulo (2024). Dynamic AMR Navigation: Simulation with Trajectory Prediction of Moving Obstacles. In 7th Iberian Robotics Conference (ROBOT 2024). Madrid: IEEE, p. 1-7. ISBN 979-8-3503-7636-4.979-8-3503-7636-4http://hdl.handle.net/10198/31143Autonomous Mobile Robots (AMRs) have significantly transformed task management in factories, warehouses, and urban environments. These robots enhance operational efficiency, reduce labor costs, and automate various tasks. However, navigating dynamic environments with moving obstacles, such as human workers, vehicles, and machinery, remains challenging. Traditional navigation systems, which rely on static maps and predefined routes, struggle to adapt to these dynamic settings. This research addresses these limitations by developing a dynamic navigation system that improves AMR performance in industrial and urban scenarios. The system enhances the A* algorithm to account for the current positions and predicted trajectories of moving obstacles, allowing the AMR to navigate safely and efficiently. Advanced sensor technologies, such as LiDAR and stereo cameras, are utilized for real-time environmental perception. The system integrates trajectory prediction and an Artificial Potential Field (APF) method for emergency collision avoidance. The solution is implemented using the Gazebo simulator and the Robot Operating System (ROS2), ensuring real-time operation and adaptive path planning. This research aims to significantly improve AMR safety, efficiency, and adaptability in dynamic environments.engAutonomous mobile robotDynamic navigationTrajectory predictionMoving obstaclesSimulationArtificial potential fieldconference paper10.1109/ROBOT61475.2024.10797420