The automatic identification system (AIS) tracks vessel movement by means of electronic exchange of navigation data between vessels, with onboard transceiver, terrestrial, and/or satellite base stations. The gathered data contain a wealth of information useful for maritime safety, security, and efficiency. Because of the close relationship between data and methodology in marine data mining and the importance of both of them in marine intelligence research, this paper surveys AIS data sources and relevant aspects of navigation in which such data are or could be exploited for safety of seafaring, namely traffic anomaly detection, route estimation, collision prediction, and path planning.

pdf/exploiting-ais-data-for-intelligent-maritime-navigation-a-4iqp8hmpde.pdf

Exploiting AIS Data for Intelligent Maritime Navigation: A Comprehensive Survey From Data to Methodology

Collision avoidance for an unmanned surface vehicle using deep reinforcement learning

The review unmanned surface vehicle path planning: Based on multi-modality constraint

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

The essence of the path planning problems is multi-modality constraint. However, most of the current literature has not mentioned this issue. This paper introduces the research progress of path planning based on the multi-modality constraint. The path planning of multi-modality constraint research can be classified into three stages in terms of its basic ingredients (such as shape, kinematics and dynamics et al.): Route Planning, Trajectory Planning and Motion Planning. It then reviews the research methods and classical algorithms, especially those applied to the Unmanned Surface Vehicle (USV) in every stage. Finally, the paper points out some existing problems in every stage and suggestions for future research.

pdf/the-review-unmanned-surface-vehicle-path-planning-based-on-48u8m0qd4x.pdf

The Review Unmanned Surface Vehicle Path Planning: Based on Multi-modality Constraint

http://www.nt.ntnu.no/users/skoge/prost/proceedings/ifac2014/media/files/1872.pdf

Collision Avoidance for Vessels Using a Low-Cost Radar Sensor

The use of random matrices for tracking extended objects has received high attention in recent years. It is an efficient approach for tracking objects that give rise to more than one measurement per time step. In this paper, the concept of random matrices is used to track surface vessels using highresolution automotive radar sensors. Since the radar also receives a large number of clutter measurements from the water, for the data association problem, a generalized probabilistic data association filter is applied. Additionally, a modification of the filter update step is proposed to incorporate the Doppler velocity measurements. The presented tracking algorithm is validated using Monte Carlo Simulation, and some performance results with real radar data are shown as well.

Probabilistic data association for tracking extended targets under clutter using random matrices

In this work a real-time grid based collision avoidance algorithm for vessels in maritime environments is presented. Most grid based collision avoidance approaches use Lee's algorithm to find an optimal collision-free path. The drawback of this algorithm is the long runtime for large grids. To reduce this runtime, in this work the A∗ search is used instead of Lee's algorithm. The algorithm additionally regards the physical constrains of the vessel and pays attention to the COLREGs. The runtime and the resulting path of Lee's algorithm and the A∗ search are compared in computational experiments. Furthermore, the performance of the collision avoidance on a real vessel on the Lake Constance is presented.

Fast grid based collision avoidance for vessels using A∗ search algorithm

In this paper, the process and results of a parameter identification task for a maritime unmanned surface vehicle are presented. The system has been modeled using state of the art methodology, and the parameters have been estimated via a weighted least square optimization approach. The required measurement data have been taken from various maneuvre trials. For solving the optimization problem, a Particle Swarm programming approach has been used, which has reliably found the global minimum of the cost function. The results show excellent agreement between measured and simulated data.

Modelling and identification of a twin hull-based autonomous surface craft

In this paper, an approach for a collision avoidance system for small Unmanned Surface Vehicles (USV) is presented. These kind of USVs are typically used for measuring tasks and thus, the collision avoidance algorithm has to take the mission objective and information about obstacles and other vessels into account. It generates an evasive trajectory that either leads around the obstacle, reduces the velocity to let another vessel pass or combines evasive manoeuvres and velocity changes. For the evasive path generation, a specialized A∗ algorithm is used that allows velocity variations and considers different turning circles for different velocities. It is applied to a grid representation of the environment that is built using a laser range finder. The laser range finder detects obstacles above the surface like other vessels, pillars or jetties. An additional tracking module is used to determine the dynamic state of moving obstacles such as other vessels. The complete collision avoidance system is integrated into a small USV and results of real world tests for environment mapping and target tracking are presented. The collision avoidance performance is shown for simulated measurement scenarios.

Mission integrated collision avoidance for USVs using laser range finder

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