Efficient marine navigation through obstructions is still one of the many problems faced by the mariner. Many accidents can be traced to human error, recently increased traffic densities and the average cruise speed of ships impedes the collision avoidance decision making process further in the sense that decisions have to be made in reduced time. It seems logical that the decision making process be computerised and automated as a step forward to reduce the risk of collision. This article reviews the development of collision avoidance techniques and path planning for ships, particularly when engaged in close range encounters. In addition, previously published works have been categorised and their shortcomings highlighted in order to identify the ' state of the art ' and issues in close range marine navigation. K E Y

a b s t r a c tUnmanned surface vehicles (USVs) have been deployed over the past decade. Current USV platforms are generally of small size with low payload capacity and short endurance times. To improve effectiveness there is a trend to deploy multiple USVs as a formation fleet. This paper presents a novel computer based algorithm that solves the problem of USV formation path planning. The algorithm is based upon the fast marching (FM) method and has been specifically designed for operation in dynamic environments using the novel constrained FM method. The constrained FM method is able to model the dynamic behaviour of moving ships with efficient computation time. The algorithm has been evaluated using a range of tests applied to a simulated area and has been proved to work effectively in a complex navigation environment.

An effective path planning or route planning algorithm is essential for guiding unmanned surface vehicles (USVs) between way points or along a trajectory. The A* algorithm is one of the most efficient algorithms for calculating a safe route with the shortest distance cost. However, the route generated by the conventional A* algorithm is constrained by the resolution of the map and it may not be compatible with the nonholonomic constraint of the USV. In this paper an improved A* algorithm has been proposed and applied to the Springer USV. A new path smoothing process with three path smoothers has been developed to improve the performance of the generated route, reducing unnecessary 'jags', having no redundant waypoints and offering a more continuous route. Both simulation and experimental results show that the smoothed A* algorithm outperforms the conventional algorithm in both sparse and cluttered environments that have been uniformly rasterised. It has been demonstrated that the proposed improved A* route planning algorithm can be applied to the Springer USV providing promising results when tracking trajectories.

SUMMARYThe increasing deployment of multiple unmanned vehicles systems has generated large research interest in recent decades. This paper therefore provides a detailed survey to review a range of techniques related to the operation of multi-vehicle systems in different environmental domains, including land based, aerospace and marine with the specific focuses placed on formation control and cooperative motion planning. Differing from other related papers, this paper pays a special attention to the collision avoidance problem and specifically discusses and reviews those methods that adopt flexible formation shape to achieve collision avoidance for multi-vehicle systems. In the conclusions, some open research areas with suggested technologies have been proposed to facilitate the future research development.

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Hybrid fuel cell and battery propulsion systems have the potential to offer improved emission performance for coastal ships with access to H 2 replenishment and battery charging infrastructures in ports. However, such systems could be constrained by high power source degradation and energy costs. Cost-effective energy management strategies are essential for such hybrid systems to mitigate the high costs. This article presents a Double Q reinforcement learning based energy management system for such systems to achieve near-optimal average voyage cost. The Double Q agent is trained using stochastic power profiles collected from continuous monitoring of a passenger ferry, using a plug-in hybrid fuel cell and battery propulsion system model. The energy management strategies generated by the agent were validated using another test dataset collected over a different period. The proposed methodology provides a novel approach to optimal use hybrid fuel cell and battery propulsion systems for ships. The results show that without prior knowledge of future power demands, the strategies can achieve near-optimal cost performance (96.9%) compared to those derived from using dynamic programming with the equivalent state space resolution.

Presently, there is increasing interest in the deployment of unmanned surface vehicles (USVs) to support complex ocean operations. In order to carry out these missions in a more efficient way, an intelligent hybrid multi-task allocation and path planning algorithm is required and has been proposed in this paper. In terms of the multi-task allocation, a novel algorithm based upon a self-organising map (SOM) has been designed and developed. The main contribution is that an adaptive artificial repulsive force field has been constructed and integrated into the SOM to achieve collision avoidance capability. The new algorithm is able to fast and effectively generate a sequence for executing multiple tasks in a cluttered maritime environment involving numerous obstacles. After generating an optimised task execution sequence, a path planning algorithm based upon fast marching square (FMS) is utilised to calculate the trajectories. Because of the introduction of a safety parameter, the FMS is able to adaptively adjust the dimensional influence of an obstacle and accordingly generate the paths to ensure the safety of the USV. The algorithms have been verified and evaluated through a number of computer based simulations and has been proven to work effectively in both simulated and practical maritime environments.

Concerns regarding the influence of the marine environment, such as surface currents and winds, on autonomous marine vehicles have been raised in recent years. A number of researchers have been working on the development of intelligent path planning algorithms to minimise the negative effects of environmental influences, however most of this work focuses on the platform of autonomous underwater vehicles (AUVs) with very little work on unmanned surface vehicles (USVs). This paper presents a novel multi-layered fast marching (MFM) method developed to generate practical trajectories for USVs when operating in a dynamic environment. This method constructs a synthetic environment framework, which incorporates the information of planning space and surface currents. In terms of the planning space, there are repelling and attracting forces, which are evaluated using an attractive/repulsive vector field construction process. The influence of surface currents is weighted against the