Ship collision avoidance methods : State-of-the-art

A path planning strategy unified with a COLREGS collision avoidance function based on deep reinforcement learning and artificial potential field

Fuzzy logic based dynamic decision-making system for intelligent navigation strategy within inland traffic separation schemes

Collision-avoidance navigation systems for Maritime Autonomous Surface Ships: A state of the art survey

A traditional A-Star (A*) algorithm generates an optimal path by minimizing the path cost. For a vessel, factors of path length, obstacle collision risk, traffic separation rule and manoeuvrability restriction should be all taken into account for path planning. Meanwhile, the water current also plays an important role in voyaging and berthing for vessels. In consideration of these defects of the traditional A-Star algorithm when it is used for vessel path planning, an improved A-Star algorithm has been proposed. To be specific, the risk models of obstacles (bridge pier, moored or anchored ship, port, shore, etc.) considering currents, traffic separation, berthing, manoeuvrability restriction have been built firstly. Then, the normal path generation and the berthing path generation with the proposed improved A-Star algorithm have been represented, respectively. Moreover, the problem of combining the normal path and the berthing path has been also solved. To verify the effectiveness of the proposed A-Star path planning methods, four cases have been studied in simulation and real scenarios. The results of experiments show that the proposed A-Star path planning methods can deal with the problems denoted in this article well, and realize the trade-off between the path length and the navigation safety.

https://www.mdpi.com/2076-3417/9/6/1057/pdf

An Improved A-Star Algorithm Considering Water Current, Traffic Separation and Berthing for Vessel Path Planning

This paper presents a real-time and deterministic path planning method for autonomous ships or Unmanned Surface Vehicles (USV) in complex and dynamic navigation environments. A modified Artificial Potential Field (APF), which contains a new modified repulsion potential field function and the corresponding virtual forces, is developed to address the issue of Collision Avoidance (CA) with dynamic targets and static obstacles, including emergency situations. Appropriate functional and safety requirements are added in the corresponding virtual forces to ensure International Regulations for Preventing Collisions at Sea (COLREGS)-constrained behaviour for the own ship's CA actions. Simulations show that the method is fast, effective and deterministic for path planning in complex situations with multiple moving target ships and stationary obstacles and can account for the unpredictable strategies of other ships. The authors believe that automatic navigation systems operated without human interaction could benefit from the development of path planning algorithms.

COLREGS-Constrained Real-time Path Planning for Autonomous Ships Using Modified Artificial Potential Fields

Estimation of berthing state of maritime autonomous surface ships based on 3D LiDAR

The accurate environment potential field (EPF) modeling method and highly efficient collision avoidance (CA) approach are key technologies for maritime autonomous surface ships (MASS). A novel and accurate environment potential field (EPF) model is proposed using electronic navigation chart (ENC) face objects to describe different types of navigable and non-navigable areas, and an improved artificial potential field (APF) method is presented to realize collaborative CA and obstacle avoidance (OA). Implicit equations of complex-shaped face objects were constructed based on $R$ -function theory, and the discrete-convex hull method was introduced to realize automatic EPF modeling. Collaborative CA and OA experiments in restricted waters were conducted on a ship handling simulator. The results show that the improved APF method can obtain a robust and deterministic collision-free path under different weather conditions and in restricted waters, and the track zone width remains within 0.1 nm. The proposed face object EPF model is efficient and accurate, even with numerous vertices and complex shapes, and can drive the ship apart at a relatively safe distance in accordance with the recommended CA parameter. We present a practical CA approach and an effective EPF modeling method for APF-based ship path planning.

https://ieeexplore.ieee.org/ielx7/6287639/6514899/09783091.pdf

Environment Potential Field Modeling for Ship Automatic Collision Avoidance in Restricted Waters

The berthing of maritime autonomous surface ships (MASSs) is a challenging operation even for an experienced captain due to the required complicated maneuvering at low speeds in a constrained water space. This article addresses safety-aware automatic berthing of MASSs moving in a constrained water region with stationary and moving obstacles. A safety-critical constrained anti-disturbance control method is proposed for the automatic berthing of MASSs subject to velocity constraints, input constraints, and collision-avoidance constraints, as well as ocean disturbances. At the kinematic level, desired linear velocities and yaw rate are prescribed by incorporating a line-of-sight guidance scheme to achieve position-heading stabilization. At the kinetic level, an anti-disturbance kinetic control law is designed based on an extended state observer. Through converting the input constraints to velocity constraints on guidance signals, the safety-guidance signals are obtained by solving a quadratic programming problem subject to the state, input, and collision-avoidance constraints. It is proven that an MASS controlled by using the proposed method is input-to-state safe in the presence of ocean disturbances. Simulation results are elaborated to substantiate the efficacy of the proposed safety-certified control law for the automatic berthing of an MASS subject to physical constraints and environmental disturbances.

Safety-Certified Constrained Control of Maritime Autonomous Surface Ships for Automatic Berthing

Sea-surface object detection is critical for navigation safety of autonomous ships. Electro-optical (EO) sensors, such as video cameras, complement radar on board in detecting small obstacle sea-surface objects. Traditionally, researchers have used horizon detection, background subtraction, and foreground segmentation techniques to detect sea-surface objects. Recently, deep learning-based object detection technologies have been gradually applied to sea-surface object detection. This article demonstrates a comprehensive overview of sea-surface object-detection approaches where the advantages and drawbacks of each technique are compared, covering four essential aspects: EO sensors and image types, traditional object-detection methods, deep learning methods, and maritime datasets collection. In particular, sea-surface object detections based on deep learning methods are thoroughly analyzed and compared with highly influential public datasets introduced as benchmarks to verify the effectiveness of these approaches. The article also proposes the direction of future research for sea-surface object detection based on EO sensors.

Yong Yin

Papers

COLREGS-Constrained Real-time Path Planning for Autonomous Ships Using Modified Artificial Potential Fields

Estimation of berthing state of maritime autonomous surface ships based on 3D LiDAR

Environment Potential Field Modeling for Ship Automatic Collision Avoidance in Restricted Waters

Safety-Certified Constrained Control of Maritime Autonomous Surface Ships for Automatic Berthing

Sea-Surface Object Detection Based on Electro-Optical Sensors: A Review