Dynamic AMR Navigation: Simulation with Trajectory Prediction of Moving Obstacles

• Type: Article in International Conference Proceedings Book
• Date: 2024

Authors

Cadete, T (Author) Other; Pinto, VH (Author) Other; Lima, J (Author) Other; Gil Manuel Gonçalves (Author) FEUP; Costa, P (Author) Other

Conference proceedings International

Title: 2024 7TH IBERIAN ROBOTICS CONFERENCE, ROBOT 2024

Pages: 1-7

7th Iberian Robotics Conference

Madrid, SPAIN, NOV 06-08, 2024

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Other information

Authenticus ID: P-018-0H6

DOI: 10.1109/robot61475.2024.10797420

Abstract (EN): Autonomous 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.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 7