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

This paper surveys the recent advances in marine mechatronic systems from a control perspective. The survey is by no means exhaustive, but introduces some notable results in marine control area. New developments in terms of control system designs for surface vessels, underwater robotic vehicles, profiling floats, underwater gliders, wave energy converters, and offshore wind turbines are briefly reviewed. In addition, a few avenues for future research are identified.

Advanced Control in Marine Mechatronic Systems: A Survey

COVID-19 cases prediction by using hybrid machine learning and beetle antennae search approach

In this paper, we propose enhancements to Beetle Antennae search &#x0028 BAS &#x0029 algorithm, called BAS-ADAM, to smoothen the convergence behavior and avoid trapping in local-minima for a highly non-convex objective function. We achieve this by adaptively adjusting the step-size in each iteration using the adaptive moment estimation &#x0028 ADAM &#x0029 update rule. The proposed algorithm also increases the convergence rate in a narrow valley. A key feature of the ADAM update rule is the ability to adjust the step-size for each dimension separately instead of using the same step-size. Since ADAM is traditionally used with gradient-based optimization algorithms, therefore we first propose a gradient estimation model without the need to differentiate the objective function. Resultantly, it demonstrates excellent performance and fast convergence rate in searching for the optimum of non-convex functions. The efficiency of the proposed algorithm was tested on three different benchmark problems, including the training of a high-dimensional neural network. The performance is compared with particle swarm optimizer &#x0028 PSO &#x0029 and the original BAS algorithm.

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BAS-ADAM: an ADAM based approach to improve the performance of beetle antennae search optimizer

Deep reinforcement learning (DRL) has excellent performance in continuous control problems and it is widely used in path planning and other fields. An autonomous path planning model based on DRL is proposed to realize the intelligent path planning of unmanned ships in the unknown environment. The model utilizes the deep deterministic policy gradient (DDPG) algorithm, through the continuous interaction with the environment and the use of historical experience data; the agent learns the optimal action strategy in a simulation environment. The navigation rules and the ship's encounter situation are transformed into a navigation restricted area, so as to achieve the purpose of planned path safety in order to ensure the validity and accuracy of the model. Ship data provided by ship automatic identification system (AIS) are used to train this path planning model. Subsequently, the improved DRL is obtained by combining DDPG with the artificial potential field. Finally, the path planning model is integrated into the electronic chart platform for experiments. Through the establishment of comparative experiments, the results show that the improved model can achieve autonomous path planning, and it has good convergence speed and stability.

An Autonomous Path Planning Model for Unmanned Ships Based on Deep Reinforcement Learning

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

Ship predictive collision avoidance method based on an improved beetle antennae search algorithm

Underactuated autonomous surface vehicles (ASVs) have stringent requirements on automatically tracking a predefined path. This paper proposes a model predictive control (MPC) approach based on adaptive line-of-sight (LOS) guidance for path following of ASVs. For the controller, a second-order nonlinear Nomoto model with disturbances is proposed as the vessel dynamic motion model after reviewing and comparing different ship motion models applied for path following control. For the guidance system, a novel adaptive LOS guidance with a variable acceptance circle radius is proposed to improve the precision of reference path tracking. Specifically, the acceptance circle radius is adapted with the angle between two adjacent straight segments of a reference path. Simulation experiments illustrate that the LOS guidance system with a variable acceptance circle radius results in smaller tracking errors compared with the fixed acceptance circle radius. The proposed path following method can track reference paths well even in the face of disturbances.

Chenguang Liu

Papers

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

Ship predictive collision avoidance method based on an improved beetle antennae search algorithm

Predictive path following based on adaptive line-of-sight for underactuated autonomous surface vessels

Dynamic anti-collision A-star algorithm for multi-ship encounter situations

A composite learning method for multi-ship collision avoidance based on reinforcement learning and inverse control