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Unmanned surface vessel (USV) based cross-domain heterogeneous formation cooperative navigation is an important method for overcoming the difficulties of navigation and positioning facing in the three areas of water surface, underwater and air in the future. This paper reviews related issues in the field of USV based cross-domain heterogeneous formation cooperative navigation from three aspects. First, this paper describes the development of USV at home and abroad, the concept and research progress of USV based cross-domain heterogeneous formation, and analyzes the cooperative navigation methods of USV based cross-domain heterogeneous formation, and summarizes them from three aspects: surface/underwater co-location, air-sea visual navigation, and path planning. Finally, it points out the key technologies and future development trends of USV based cross-domain heterogeneous formation.

ObjectiveThe artificial bee colony (ABC) algorithm has such characteristics as few control parameters, strong local optimization ability and fast convergence speed. However, when solving path optimization problems, it can easily fall into local optimal solutions. In order to solve the problem of pipeline routing in a ship pipeline system, an improved artificial bee colony (IABC) algorithm is proposed. MethodBased on the traditional artificial bee colony algorithm, the crossover operation of genetic operators is introduced into the update mechanism of following bees, and an adaptive strategy is adopted for the crossover probability of the crossover operator. The crossover operation on the population is used to find new solutions in the global range. The way scout bees search for new paths is improved from updating the points that the path passes to updating the "road sections" in the path. This paper proposes an artificial bee colony co-evolution algorithm for solving the optimization of branch pipeline paths. ResultsCompared with the standard artificial bee colony algorithm, the improved algorithm can improve the path layout effect by 32.3%–37.4% and the convergence speed by 17.7%–29.9%. ConclusionThe improved artificial bee colony algorithm proposed herein has higher solution quality, faster convergence speed and better stability than the traditional artificial bee colony algorithm for a single pipe or branch pipe.

ObjectiveThis paper develops a multi-stage path planning algorithm for the auto docking operation of an underactuated surface vehicle (USV). As one of the most difficult tasks, docking requires advanced maneuvering skills. Therefore, it is necessary to generate a smooth trajectory in the berthing environment while maintaining control accuracy. MethodThe proposed method introduces a hybrid A* search algorithm in the pre-docking stage in which the well-known A* search algorithm is combined with the kinematic state space of the USV using the Reeds–Shepp curves, enabling it to produce sub-optimal paths that guarantee kinematic feasibility and low collision risk. For the docking stage, a cubic Bezier curve is introduced to represent the target path. The generated trajectories are then tested in numerical simulations wherein a mathematical model of a ship with two fixed propellers is established and verified. ResultsThe simulation and experimental results show that the path planning and tracking performance of the method are satisfactory.ConclusionThe proposed path planning method can contribute to automatic ship docking and the development of smart ships.

The underwater glider (UG) is a new type of underwater vehicle driven by buoyancy, which has the advantages of low energy consumption, high efficiency, long-endurance, low cost, reusability and so on. The UG can also meet the needs of long-term and large-scale ocean observation and exploration. As an observation platform, the UG needs to carry out path planning and correction continuously in the early stages and during missions in order to better serve the requirements of ocean observation and exploration. First, this paper summarizes the relevant literatures on path planning research methods for UG in recent years. UG path planning algorithms are mainly divided into three categories: traditional algorithms, intelligent optimization algorithms and multi-algorithm fusions. Combined with practical application, the performance of different path planning algorithms is compared. The key technologies of UG path planning, such as environment reconstruction, environment perception, intelligent decision-making and underwater positioning, are then summarized. Finally, the development direction of UG multi-algorithm integration, multi-glider cooperation, multi-dimensional integration of spatiotemporal constraints and high-precision in complex and unsteady environments are prospected.

ObjectiveIn order to solve the multi-objective collision avoidance problem of unmanned surface vehicles (USVs) in open waters, this paper takes a medium-sized USV as the object and carries out the research and application exploration of a local path planning algorithm based on the Convention on the International Regulations for Preventing Collisions at Sea (COLREGS). MethodsA virtual obstacle line method is proposed to load the constraints of COLREGS based on the RRT algorithm for meeting the practical requirements of collision avoidance path planning in open water. In light of the problem that the RRT algorithm does not consider the speed dimension, a velocity obstacle (VO) algorithm is introduced. Next, a VO-RRT fusion algorithm is proposed and given the most dangerous obstacle strategy in order to solve the problem of real-time collision avoidance under multi-objective conditions. ResultsThe simulation and real ship test results show that the proposed algorithm has better real-time performance and takes less than 50 ms to undertake path re-planning. The collision avoidance path planned by the algorithm meets the relevant requirements of Articles 6, 8 and 13–18 of COLREGS. The proposed method can effectively deal with the multi-target collision avoidance problem in open waters. ConclusionThe proposed method has good rule compliance, real-time and safety performance, and high practical value.

Path planning is one of the research hotspots of unmanned surface vehicles (USVs) and a key technology for realizing their autonomy and intelligence. Aiming at the problem of local path planning for USVs, this paper elaborates on the application of various algorithms at the two levels of path search and trajectory optimization, introduces the latest research results in recent years and carries out an in-depth analysis of the characteristics and deficiencies of various algorithms. It then summarizes an improved method to adapt the algorithm to the motion characteristics of USVs, and finally gives a brief outlook on the development trends of local path planning for USVs, providing some ideas for future research in this field.

The Autonomous Underwater Vehicle(AUV)is an important tool for ocean exploration and the exploitation of underwater resources,which plays important roles in civilian and military fields. Along with the research progress of AUVs,it has become the current development trend to cooperate on completing underwater operations by constructing the Multiple Autonomous Underwater Vehicle(MAUV)system. The MAUV system has important theoretical research significance and practical value for improving the intelligence level of underwater vehicles and developing marine equipment. In this paper,the state of the art of the MAUV is presented from the point of view of practical application and scientific research. The main methods of path planning and cooperative control for MAUV are illustrated,including artificial intelligence and formation control techniques. Finally,the research trends of MAUV are also discussed and the main topic is highlighted.