This paper presents a multimodal maritime dataset and the data collection procedure used to gather it, which aims to facilitate autonomous navigation in restricted water environments. The dataset comprises measurements obtained using various perception and navigation sensors, including a stereo camera, an infrared camera, an omnidirectional camera, three LiDARs, a marine radar, a global positioning system, and an attitude heading reference system. The data were collected along a 7.5-km-long route that includes a narrow canal, inner and outer ports, and near-coastal areas in Pohang, South Korea. The collection was conducted under diverse weather and visual conditions. The dataset and its detailed description are available for free download at https://sites.google.com/view/pohang-canal-dataset .

This article is concerned with the robust formation tracking control of a group of underactuated surface vessels whose formation is characterized by the relative bearings between the vessels. Control protocols are proposed to asymptotically steer certain points on the vessels, called hand points , which lie ahead of their centers of mass along the longitudinal axes, to track the desired moving formation; such a control method is called an output regulation approach . The moving formation is guided by the motions of two or more agents, which are referred to as leaders , and the other follower agents track the leaders by maintaining bearing constraints to neighbors. The proposed control schemes are designed based on a backstepping technique and sliding mode control. The latter is employed to attenuate known bounded disturbances present in the vessels' dynamics. When the bounds of the disturbances are unknown, an adaptive control protocol is subsequently investigated, under which the system is shown to converge to a small neighborhood of the target formation. Further, sufficient conditions on the upper bound of the hand points' desired velocities are provided for the uniform ultimate boundedness of the vessels' internal sway-yaw dynamics. Simulation results are provided to support the theoretical analysis.

In this article, we describe the development process of autonomous navigation capabilities of a small cruise boat operating in a canal environment and present the results of a field experiment conducted in the Pohang Canal, South Korea. Nonlinear model predictive control (NMPC) was used for the online trajectory planning and tracking control of the cruise boat in a narrow passage in the canal. To consider the nonlinear characteristics of boat dynamics, system identification was performed using experimental data from various test maneuvers, such as acceleration–deceleration and zigzag trials. To efficiently represent the obstacle structures in the canal environment, we parameterized the canal walls as line segments with point cloud data, captured by an onboard LiDAR sensor, and considered them as constraints for obstacle avoidance. The proposed method was implemented in a single NMPC layer, and its real-world performance was verified through experimental runs in the Pohang Canal.

This letter presents an integrated navigation and control strategy for an autonomous surface vehicle (ASV) to operate in narrow waterways without relying on GPS. The proposed method uses a camera and a light detection and ranging (LiDAR) sensor to detect navigable regions in the waterway. A deep learning-based semantic segmentation algorithm is applied to detect the navigable region in camera images, and the segmented region is projected onto the water surface using planar homography. A line-detection algorithm is also introduced to improve the reliability of detecting navigable regions from LiDAR measurements. A safe collision-free path for the ASV is generated within the navigable regions using model predictive control-based local path planning and control algorithms. The performance and practical utility of the proposed method were demonstrated through field experiments using a small cruise boat, modified as an autonomous surface vehicle.

This brief presents a cascade tube-based model predictive control (CTMPC) approach for safe ship berthing in the presence of unknown and bounded disturbances. The nonlinear dynamic model of a ship moving at low speeds is decomposed into a linear time-invariant (LTI) model for the surge system and a linear parameter-varying (LPV) model for the sway-yaw system, where the surge velocity is a scheduling parameter. The tube-based model predictive control (MPC) problems of these models are solved using the proposed CTMPC approach. The robust positively invariant (RPI) sets of the two subsystems and the geometry of the predefined separation area around the ship are considered to calculate the safe region of the ship under bounded disturbances. The proposed control architecture is shown to be both recursively feasible and stable, and simulation results are presented to verify its practical effectiveness.