Autonomous collision detection and avoidance for ARAGON USV: Development and field tests

TLDR

The study develops multi‑target detection and tracking algorithms using sensor fusion for autonomous navigation and collision avoidance on the USV Aragon, and evaluates them through field experiments. Radar, lidar, and camera sensors feed automatic ship detection algorithms into sensor‑level tracking filters, whose outputs are fused centrally to provide persistent target tracking and to generate COLREG‑compliant collision‑avoidance maneuvers. Field experiments demonstrate that the integrated system achieves reliable target tracking and effective collision avoidance for the USV Aragon.

Abstract

Abstract This study addresses the development of algorithms for multiple target detection and tracking in the framework of sensor fusion and its application to autonomous navigation and collision avoidance systems for the unmanned surface vehicle (USV) Aragon. To provide autonomous navigation capabilities, various perception sensors such as radar, lidar, and cameras have been mounted on the USV platform and automatic ship detection algorithms are applied to the sensor measurements. The relative position information between the USV and nearby objects is obtained to estimate the motion of the target objects in a sensor‐level tracking filter. The estimated motion information from the individual tracking filters is then combined in a central‐level fusion tracker to achieve persistent and reliable target tracking performance. For automatic ship collision avoidance, the combined track data are used as obstacle information, and appropriate collision avoidance maneuvers are designed and executed in accordance with the international regulations for preventing collisions at sea (COLREGs). In this paper, the development processes of the vehicle platform and the autonomous navigation algorithms are described, and the results of field experiments are presented and discussed.