Multi Object Tracking for Predictive Collision Avoidance

Bruk Gebreyewhans Gebregziabher

University of Girona, Spain

Biography :

Bruk has completed his joint MSc in Intelligent Field Robotic Systems from university of Gironaa, with University of Zagreb. BSc In Computer Science and Engineering from Mekelle University. He is currently working as robotics engineer specializing in SLAM.

Abstract :

The safe and efficient operation of Autonomous Mobile Robots (AMRs) in complex environments, such as manufacturing, logistics, and agriculture, necessitates accurate multi-object tracking and predictive collision avoidance. This paper presents algorithms and techniques for addressing these challenges using Lidar sensor data, emphasizing ensemble Kalman filter. The developed predictive collision avoidance algorithm employs the data provided by Lidar sensors to track multiple objects and predict their velocities and future positions, enabling the AMR to navigate safely and effectively. A modification to the dynamic windowing approach is introduced to enhance the performance of the collision avoidance system. The overall system architecture en­compasses object detection, multi-object tracking, and predictive collision avoidance control. The experimen­tal results, obtained from both simulation and real-world data, demonstrate the effectiveness of the proposed methods in various scenarios, which lays the foundation for future research on global planners, other control­lers, and the integration of additional sensors. This thesis contributes to the ongoing.