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Abstract(s)
Autonomous Surface Vehicles (ASVs), operating near ship harbors or relatively close to shorelines must be able to steer away from incoming vessels and other possible obstacles, be they dynamic or not. To do this, one must implement some type of multi-target tracking and obstacle avoidance algorithms that lets the vehicle dodge obstacles. This thesis presents a radar-based multi-target tracking system developed for obstacle detection and monitoring. The proposed architecture system can use different types of sensors to improve the quality of the data. This work is focused in the radar sensor. The system was designed for ROAZ II ASV belonging to INESC TEC/ISEP and implemented in Robot Operating System (ROS) for easier integration with the already existing software. The developed aggregation, classification and tracking algorithms are presented, as well as the algorithm for estimation of possible collisions between vessels. Aggregation and classification algorithms were tested with real data and the results are presented in this work. A simulation environment could prove the correct behavior of tracking and estimation of possible collisions algorithms.
Description
Keywords
Data aggregation Multi-Target Tracking Kalman filter