Fast Collision Avoidance Method Based on Velocity Resolution for Unmanned Surface Vehicle

There are many unknown obstacles in the sea, so the autonomous navigation of unmanned surface vehicle needs to avoid them as soon as possible even under the condition of low control ability of the controller. To solve the problem, by combining the dynamic collision avoidance algorithm and tracking control, a dynamic collision avoidance control method in the unknown ocean environment is presented. In this article, in consideration of the unknown ocean environment and real-time dynamic obstacle avoidance problem, the collision avoidance controller using a velocity resolution method and backstepping tracking controller based on unmanned surface vehicle maneuvering motion model is designed. Simulation results show that the method is effective and accurate and can provide the reference for the unmanned surface vehicle intelligent collision avoidance control technology.

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.

In recent years, with the development of unmanned platforms, unmanned surface vehicles (USV) are attracting more and more attention. Compared to ordinary ships, USV have a smaller volume and faster speed, so their collision avoidance system (CAS) should have better responsiveness and stability. The paper describes a method that is based on finite control set model predictive control (FCS-MPC). A finite control set is generated by more practical control commands: the thruster speed and propulsion angle of the USV. The method is conceptually and computationally simple and yet quite versatile, as it can account for the dynamics of the USV, steering and propulsion system. Based on the theory of FCS-MPC, a safe and fast CAS is proposed, and it is verified in different static and dynamic environments. The real environment model for collision avoidance is established by extracting the environment data from the electronic chart. The result shows that the method is effective and can control the USV to sail safely and quickly in complex real scenarios with multiple dynamic obstacles.

Abstract: Path planning is a way to define the motion of an autonomous surface vehicle (ASV) in any existing obstacle environment to enable the vehicle's movement by setting directions to avoid that can react to the obstacles in the vehicle's path. A good, planned path perceives the environment to the extent of uncertainty and tries to build or adapt its change in the path of motion. Efficient path planning algorithms are needed to alleviate deficiencies, which are to be modified using the deterministic path that leads the ASV to reach a goal or a desired location while finding an optimal solution has become a challenge in the field of optimization along with a collision-free path, making path planning a critical thinker. The traditional algorithms have a lot of training and computation, making it difficult in a realistic environment. This review paper explores the different techniques available in path planning and collision avoidance of ASV in a dynamic environment. The objective of good path planning and collision avoidance for a dynamic environment is compared effectively with the existing obstacle’s movement of different vehicles. Different path planning technical approaches are compared with their performance and collision avoidance for unmanned vehicles in marine environments by early researchers. This paper gives us a clear idea for developing an effective path planning technique to overcome marine accidents in the dynamic ocean environment while choosing the shortest, obstacle-free path for Autonomous Surface Vehicles that can reduce risk and enhance the safety of unmanned vehicle movement in a harsh ocean environment.

Global-local hierarchical path planning scheme for unmanned surface vehicles under dynamically unforeseen environments

A novel intelligent collision avoidance algorithm based on deep reinforcement learning approach for USV

In this paper, the intelligent cooperative control problem of unmanned surface vehicles (USVs) is investigated. We consider a team of USVs used in the protection of a target against potential threats. The task of the USVs is to cooperatively protect the target from being destroyed. A behavior-based fuzzy logic control system is presented to solve the problem. First, we identify two types of behaviors; low and high level behaviors. High level behaviors act as a mechanism to induced coordination and synchronization among USVs. Based on this, four high level behaviors (behavior integrator, intercept, formation and assist behaviors), each implementing a fuzzy inference system and two low level behaviors(collision avoidance and attack behaviors) are proposed. The paper defines behavior-based rules to meet the requirement of a simple operational scenario. Genetic algorithms is used to learn the rule base and the choice of behavior. Simulation results demonstrates the effectiveness of our approach. Results shows that the approach has potential application for a real team of cooperative USVs.

Unmanned Surface Vehicles (USVs) are intelligent machines that have been widely studied in recent years. The safety of USVs’ activities is a priority issue in their applications; one effective method is to delimit an exclusive safety domain around the USV. Besides considering collision avoidance, the safety domain should satisfy the requirements of encounter situations in the COLREGs (International Regulations for Preventing Collisions at Sea) as well. Whereas the model providing the safety domain for the USVs is defined through the experience of the manned ships, a specific model for USVs has been rarely studied. A dynamic navigation safety domain (DNSD) for USVs was proposed in this paper. To construct the model, the essential factors that could affect the navigation safety of the USVs were extracted via a rough set, and the extension functions of these factors were carried out. The DNSD was employed in various situations and compared with the ship domain models of common ships. It was found that the domain boundary can be automatically corrected according to the change in the working conditions when the DNSD is in use. Compared with the Fujii and Coldwell models, the DNSD can provide a larger safety area for a USV’s action of collision avoidance.

When sailing on cluttered water, effective collision avoidance is an essential safety requirement for unmanned surface vehicles (USVs). So that it can avoid multiple random ships effectively. This paper proposes a collision avoidance strategy for USVs based on the probabilistic game theory framework. First, a probability model is established combined with the USV’s kinematics for the collision avoidance problem. Then a greedy algorithm is designed under the framework of probability. It uses a cost function that searches possible movement paths to determine avoidance strategies for the USV. Compared with other collision avoidance methods, the method in this paper seeks to minimize the collision probability instead of guaranteeing no collision, because it is very difficult or impossible to completely avoid the collision in practice. Based on this, our method copes with kinematic constraints of the USV. The effectiveness of the algorithm is verified by simulation, and the simulation results show that the collision avoidance strategy proposed in this paper achieves a very good collision avoidance performance.

Path planning and obstacle avoidance of Unmanned Surface Vehicle (USV) is one of the hottest research topics in modem national defense and ocean engineering. Considering the issue of obstacle-free path planning of USV, this paper focuses on a 3-DoF USV and develops an algorithm design. We adopt Gauss pseudo-spectral method to discretize control model and make use of a hybrid algorithm to optimize which combines the advantage of genetic algorithm and sequential quadratic programming algorithm. Simulation results show that this method can quickly explore a high-precision route in an unknown environment which meets the mobility requirement of USV without setting the initial value artificially.

With the development of marine technology, the research of unmanned surface vehicles (USVs) has received wide attention from the world. Designing effective collision avoidance algorithm in dynamic sea environment is necessary for the navigation of USVs. This paper describes a collision avoidance system (CAS) with COLREGs compliance to improve autonomous navigational ability of USV. For small podded propulsion USV, a three-DOFs maneuvering model is established and the parameters of model are determined by the parameter identification method based on field experiments. About the COLREGs rules, the different maneuvering behaviors are summed up according to encounter situation and collision risk assessment. In the CAS, finite control set model predictive control is adopted, and COLREGs rules are taken as an evaluation standard in the quality function. Finally, the system is verified to avoid multiple dynamic obstacles and compliance vessel in the simulation and real environment, and the results of collision avoidance under four encounter situations are described in detail.

A consensus algorithm for coordinated control of unmanned surface vehicles (USVs) is studied. The USVs have different driving directions and initial velocities, so the consensus algorithm is needed to coordinate the directions and velocities of the USVs. Firstly, based on the first order linear model of USVs, get the first order nonlinear differential equations of high frequency, and the simulate environment of USVs. Then, based on the Vicsek model, the consensus algorithm in cooperative control of USVs is proposed. Aiming at the problem that USV's angular velocity lags behind the rudder angle, this paper improves the traditional PD control method, and adopts cascade PID control. So, USV can reach the same driving direction in a shorter time. Finally, simulations prove that the algorithm is feasible and compare the difference of efficiency between the PD controller and the cascade PID controller.

We consider an unmanned surface vehicle (USV) whose mission is to approach a maneuvering target ship as fast as possible followed by monitoring the target closely. The USV is equipped with sensors to detect the relative position of the target ship. Also, we assume that the USV is faster than the target and is localized using global positioning system. To achieve the mission, we develop a control law to make the USV approach the designated point (e.g., stern, left side, or right side, etc.) of the target as fast as possible. Once the USV reaches the designated point of the target, the USV checks whether the speed of the USV is fast enough to circle around the target. In the case where the circling maneuver is not feasible, the USV keeps following the designated point of the target. Once the USV determines that the circling maneuver is feasible, then we make the USV circle around the target for close monitoring, while maintaining a designated distance from the target. We present MATLAB simulations to verify our approach.

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.

Efficient maritime search and rescue (SAR) is crucial for responding to maritime emergencies. In traditional SAR, fixed search path planning is inefficient and cannot prioritize high-probability regions, which has significant limitations. To solve the above problems, this paper proposes unmanned surface vehicles (USVs) path planning for maritime SAR based on POS-DQN so that USVs can perform SAR tasks reasonably and efficiently. Firstly, the search region is allocated as a whole using an improved task allocation algorithm so that the task region of each USV has priority and no duplication. Secondly, this paper considers the probability of success (POS) of the search environment and proposes a POS-DQN algorithm based on deep reinforcement learning. This algorithm can adapt to the complex and changing environment of SAR. It designs a probability weight reward function and trains USV agents to obtain the optimal search path. Finally, based on the simulation results, by considering the complete coverage of obstacle avoidance and collision avoidance, the search path using this algorithm can prioritize high-probability regions and improve the efficiency of SAR.