Field experiment of autonomous ship navigation in canal and surrounding nearshore environments

Abstract

In this paper, we present the development of autonomous navigation capabilities for small cruise boats, and their verification by field experiments in a canal and its surrounding waters. A cruise boat was converted to an autonomous surface vehicle (ASV) by installing various sensors and actuators to enable autonomous navigation. Navigation and perception sensors, such as global positioning system, attitude and heading reference system, radar, light detection and ranging (LiDAR), and cameras, were mounted on the ASV to estimate its motion and perceive the surrounding environment. Motors and potentiometers were installed for active control of the ASV. Software system components including navigation filters, object‐detection, path‐planning, and control algorithms were designed and implemented. In the narrow canal region, LiDARs were used to detect the side walls and boundaries of the canal. In open areas outside the canal, obstacles and object features were detected using various combinations of onboard sensors. A model‐based path‐planning algorithm was designed to avoid the detected obstacles, and the line‐of‐sight guidance was employed to control the vehicle. The performance of the developed system was verified through a field experiment in a real‐world maritime environment.

Robust Decision-Making for the Reactive Collision Avoidance of Autonomous Ships against Various Perception Sensor Noise Levels

“…Han et al [41] used EKF on RADAR measurements to predict the motion of obstacles. Stanislas et al and Kim et al [42] filtered RADAR and LIDAR measurements by experimentally tuning the intensity of basic RADAR settings and filtering out LIDAR measurements that exceeded a predefined threshold to estimate the state of obstacles. However, the primary focus of these studies was limited to filtering out sensor noise to enhance the situation awareness for the state estimation of the obstacles.…”