A constrained locking sweeping method and velocity obstacle based path planning algorithm for unmanned surface vehicles in complex maritime traffic scenarios

To ensure navigation safety, unmanned surface vehicles (USVs) need to have autonomous collision avoidance capability. A large number of studies on ship collision avoidance are available, and most of these papers assume that the target ships keep straight or follows the International Regulations for Preventing Collisions at Sea (COLREGS). However, in the actual navigation process, the target ship may temporarily turn. Based on the above reasons, this paper proposes a multi-ship collision avoidance decision method for USVs based on the improved velocity obstacle algorithm. In the basic dynamic ship domain model, a collision risk model is constructed to improve the accuracy of the risk assessment between the USV and target ships. The velocity obstacle algorithm is combined with the dynamic ship domain, and the collision avoidance timing and method are judged according to the collision risk. The simulation results show that the decision method can handle the situation that the target ship temporarily turns and has an emergency collision avoidance capability. Compared with the traditional VO algorithm, the collision avoidance time of the method is shorter, and the number of course changes is less.

With the development of technologies related to unmanned vehicles, including unmanned aerial vehicle and unmanned surface vehicle, unmanned vehicles play a pivotal role in maritime management and cruise activities. Meanwhile, lots of evidence shows that a single unmanned vehicle is not qualified for operation under complex environment, whereas the unmanned aerial vehicle–unmanned surface vehicle formation consists of multiple unmanned aerial vehicles and unmanned surface vehicles, which can enhance the performance of the single unmanned vehicle significantly. However, the cooperative operation performance of the unmanned aerial vehicle–unmanned surface vehicle formation is constrained by its communication capacity. Consequently, a cooperative communication framework is designed for the unmanned aerial vehicle–unmanned surface vehicle formation. A distributed dynamic network topology is constructed for fulfilling effective communication for the unmanned aerial vehicle–unmanned surface vehicle formation based on ad hoc network. Experiments are carried out by employing the built unmanned aerial vehicle–unmanned surface vehicle formation platform. Results verify the superiority of the designed cooperative communication framework for the unmanned aerial vehicle–unmanned surface vehicle formation.

With economic globalization, ships tend to be larger and faster, and the volume of maritime traffic is increasing. Ships sailing in waters with dense traffic flow are easy to fall into complicated multi-ship encounter situations and have a high risk of collision. Thus, it is crucial to conduct risk analysis in such situations. In this paper, a modified collision analysis method for detecting dangerous multi-ship encounters in ports and waterways is proposed. The velocity obstacle algorithm is utilized to detect encounters. The model of the elliptic ship domain was integrated into the algorithm as the criteria. The Boolean operation was also used in the multi-ship encounter. A case study is conducted to illustrate the efficacy of the improved model, and a comparison between the existing method and the formal model is also performed. The results indicate that with the integration of the ship domain, the proposed method can effectively detect the encounters of multiple ships which are dangerous to collide.

Unmanned surface vehicle (USV) path planning is a crucial technology for achieving USV autonomous navigation. Under global path planning, dynamic local obstacle avoidance has become the primary focus for safe USV navigation. In this study, a USV autonomous dynamic obstacle avoidance method based on the enhanced velocity obstacle method is proposed in order to achieve path replanning. Through further analysis of obstacles, the obstacle geometric model set in the conventional velocity obstacle method was redefined. A special triangular obstacle geometric model was proposed to reconstruct the velocity obstacle region. The collision time was predicted by fitting the previously gathered data to the detected obstacle’s distance, azimuth, and other relevant data. Then, it is combined with the collision risk to determine when obstacle avoidance should begin and end. In order to ensure safe driving between path points, the international maritime collision avoidance rules (COLREGs) are incorporated to ensure the accuracy of obstacle avoidance. Finally, through numerical simulations of various collision scenarios, it was determined that, under the assumption of ensuring a safe encounter distance, the maximum change rates of USV heading angle are optimized by 17.54%, 58.16%, and 28.63% when crossing, head-on, and overtaking, respectively. The results indicate that, by optimizing the heading angle, the enhanced velocity obstacle method can avoid the risk of ship rollover caused by an excessive heading angle during high-speed movement and achieve more accurate obstacle avoidance action in the event of a safety encounter.

Unmanned surface vehicle (USV) has witnessed a rapid growth in the recent decade and has been applied in various practical applications in both military and civilian domains. USVs can either be deployed as a single unit or multiple vehicles in a fleet to conduct ocean missions. Central to the control of USV and USV formations, path planning is the key technology that ensures the navigation safety by generating collision free trajectories. Compared with conventional path planning algorithms, the deep reinforcement learning (RL) based planning algorithms provides a new resolution by integrating a high-level artificial intelligence. This work investigates the application of deep reinforcement learning algorithms for USV and USV formation path planning with specific focus on a reliable obstacle avoidance in constrained maritime environments. For single USV planning, with the primary aim being to calculate a shortest collision avoiding path, the designed RL path planning algorithm is able to solve other complex issues such as the compliance with vehicle motion constraints. The USV formation maintenance algorithm is capable of calculating suitable paths for the formation and retain the formation shape robustly or vary shapes where necessary, which is promising to assist with the navigation in environments with cluttered obstacles. The developed three sets of algorithms are validated and tested in computer-based simulations and practical maritime environments extracted from real harbour areas in the UK.

Path planning is one of the key technologies for unmanned surface vehicle (USV) to realize intelligent navigation. However, most path planning algorithms only consider the shortest path length and ignore other constraints during the navigation, which may generate a path that is not practically optimal in the view of safety and angular constraints. To solve this problem, an optimal path planning algorithm for USV based on fast marching method (FMM) is proposed in this paper. Firstly, a arrival time potential field with the only minimum point is generated by executing FMM on the initial binary map, which is used as a basic model for path planning. Secondly, to modeling the safety and heading angle constraints of the vehicle, the saturated safety map based on SFMS and the virtual obstacles formed by USV's turning ability are introduced and applied respectively. Thirdly, the gradient descent method is used on the improved potential field to search out an optimal path that can well meet the actual constrains of USV. The effectiveness and rationality of the algorithm are then verified by simulations.

Collision risk assessment in real-time is of critical importance to prevent ship collision at sea. Therefore, this paper presents a real-time collision risk assessment method based on an improved Non-linear Velocity Obstacle (NL-VO) algorithm. The proposed method consists of two main modules: trajectory uncertainty module assumes that the increase in the radius of the circular restricted area is consistent with the Wiener process; collision risk assessment module utilizes the NL-VO algorithm combined with the dynamic circular restricted area to evaluate the potential collisions. Several encounter scenarios in a specific port channel are designed to demonstrate the feasibility of using this proposed method to assess ship collision risk in real-time. The results are visualized and illustrated that this improved NL-VO algorithm can timely identify a ship that is in danger of colliding with the own ship and know when the collision will occur, which contributes to reducing ship collision occurrence probability.

Efficient cleaning of floating waste using an intelligent unmanned surface vehicle is an important development trend in inland water governance. Path planning is the core of the decision-making module for unmanned surface vehicle waste cleaning and is key to achieving autonomous operation of the unmanned surface vehicle. However, due to the complexity and dynamic changes of the water surface environment, unmanned surface vehicle path planning methods for floating waste face challenges such as small size, uncertainty, and uneven distribution of floating waste. In response to the above issues, this article studies the problem of insufficient integration and low efficiency between existing path planning algorithms and target-perception modules, and designs an efficient overall path planning method for floating-waste cleaning by an unmanned surface vehicle. This method transforms the path planning problem of floating-waste cleaning unmanned surface vehicle into a Traveling Salesman Problem by setting global patrol points and tracking local targets, and proposes an improved ant colony algorithm, IACO, to solve the Traveling Salesman Problem. This article is based on the TSPLIB dataset and practical applications for experiments. The experimental results show that the proposed method has average optimal path lengths of 75.930 m, 446.555 m, and 703.759 m on the Ulysses22, eil51, and st70 datasets, respectively, which are reduced by 0.355 m, 4.108 m, and 13.575 m compared to the benchmark.

The Unmanned Surface Vehicles (USVs) have the characteristics of small volume, fast speed, and its application field is often in unknown complex ocean environment. Therefore, it is necessary to solve the fast collision avoidance problem of USV in unknown ocean environment. To solve this problem, a fast collision avoidance method based on velocity resolution is proposed. The motion constraint of the USV in the method is mainly based on the Lanxin USV of Dalian Maritime University. Finally, the safety and rapidity of the method are verified in the complex static and dynamic environments, which can provide a reference for the research of collision avoidance technology about USV.

The ant colony optimization (ACO) algorithm is improved and further integrated with the immune algorithm (IA) to address its problems, such as slow convergence, local optimum, and premature convergence in the path planning. An algorithm integrating IA and improved ant colony optimization (IACO) is, therefore, put forward to realize the optimal planning of global path for an unmanned surface vehicle (USV). First, the ACO algorithm was improved in three aspects, that is, generation of initial pheromones, transition probability, and update of pheromones. The proposed IA-IACO algorithm combined the advantages of IA and IACO, sped up the convergence, and enhanced the optimization capability and operational efficiency. Second, the IA-IACO algorithm was designed and applied in the global path planning of an unmanned surface vehicle, achieving great global optimization and convergence. Finally, a path smoothing algorithm was devised to achieve the implementable, economic, and stable path while guaranteeing the safe navigation of the USV. A simulation test was carried out to prove the effectiveness and superiority of the designed global path planning algorithm in the practical engineering.

USV path planning algorithm based on plant growth

FDA's Division of Cooperative Programs (DCP) has used non-computerized instrumentation extensively and proficiently for its specialized coastal water pollution studies that assist state management of shellfish harvest waters. This paper summarizes the instrumentation development portion of a September 1998 fluorometric study in coastal Connecticut. Fluorometry was used to study estuarine dispersion, travel time, and dilution of wastewater treatment plant discharge, yet the computerized hydrography system (CHS) can be used with any instrument that supports real-time ASCII (or NMEA 0183) output. Leading up to this instrumentation development were technology advancements for geographical positioning systems (GPS), fluorometers, portable PCs, electronic nautical charts, and navigation and hydrographic software. Prior to this study, only one off-the-shelf software application (Chartview Pro) was found that had a feature intended to computer log and display instrument data correlated to a plot of the vessel track on electronic nautical charts, both real-time and after the study. Yet this hydrographic feature had not been implemented by an end-user, until FDA-DCP staff developed a computerized hydrography system (CHS) that included Chartview Pro, and the software developer responded to feedback from FDA-DCP instrumentation trials. Stability as well as the responsiveness of the software company were some criteria for the CHS software, as was low cost, and therefore the off-the-shelf appeal. Versatility was also important. A purpose of developing the CHS was to update the hydrography instrumentation used by FDA-DCP and its cooperative program partners.

Path planning based on A* algorithm for unmanned surface vehicle (USV) is researched in this paper. The kinematic model of the USV is built and simplified under the reasonable assumptions. According to the position and orientation coordinates of possible obstacle, the collision conditions and workspace of unmanned surface vehicle are analyzed. For the path planning of USV, A* algorithm is designed to realize the effective and efficient motion. The collision-free path search in the free workspace of USV is carried out by using A* algorithm, and the obstacle avoidance path planning is analyzed. Then the simulation results prove the effectiveness of A* algorithm for path planning to avoid the obstacle of USV.

Unmanned surface vehicles (USVs) are crucial in ensuring maritime safety and observation, attracting widespread attention and research. However, a single USV exhibits limited performance and cannot effectively observe complex marine environments. In contrast, clusters of USVs can collaborate to execute complex maritime tasks, thereby enhancing the overall operational efficiency. USVs typically form heterogeneous clusters by combining vehicles with varying maneuverabilities and communication network capabilities. This has sparked an increased interest in cooperative communication research within heterogeneous USV clusters. The heterogeneous USVs discussed in this paper share the same dynamic model; however, they differ in dynamic parameters and communication capabilities. First, this study establishes a three-degree-of-freedom motion mathematical model for an underdriven USV considering environmental interference. It estimates the dynamic parameters of four USVs and evaluates their communication abilities, laying the foundation for researching the cooperative control of heterogeneous USV clusters and their application in Ocean Observation. Next, the communication capability of the USVs is assessed by studying the communication mode and signal transmission loss within the USV clusters. This study investigates the problem of cooperative communication in USV cluster formation, starting with the communication delay of USV clusters under a directed switching topology. Finally, a coherent formation controller is designed under a switching communication topology to address the dynamic transformation of communication topologies within heterogeneous USV clusters. This verifies that heterogeneous USV clusters can seamlessly form and maintain formation shapes during communication topology transformations through formation simulation experiments involving four heterogeneous USVs was 22% higher than that of dispersed control topology structures. This study provides a solid foundation for future investigations into the cooperative control of heterogeneous USV clusters and their applications in marine observations.

Unmanned surface vehicles have the advantages of maneuverability, concealment, wide activity area and low cost of use. Therefore, they have broad application prospects. This makes unmanned surface vehicles a research hotspot at home and abroad, and the sensing technology is the basis for the unmanned surface vehicles to perform tasks. The perception technology based on optical vision has the advantages of convenient application, relatively low cost, easy data acquisition and large amount of information, and has been widely studied by scholars at home and abroad. This paper mainly discusses the research of optical vision in unmanned surface vehicles from five aspects: Firstly, the water surface image preprocessing based on unmanned surface vehicles, mainly including water surface image defogging enhancement research; second is the use of light vision target detection; the next is the surface target tracking methods. Finally, the light vision research of unmanned surface vehicles is summarized and forecasted.