Motion planning for unmanned surface vehicle based on a maneuverability mathematical model

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 hybrid path optimization method of unmanned surface vehicle (USV) with underwater acoustic communication constraint combines with energy attenuation characteristic, which minimizes the sail time of USV and also ensures that USV can collect the marine information of all the seabed-bases. The path planning of USV is similar to the traveling salesman problem (TSP). Genetic algorithm is used to optimize the path preliminarily. Traditional genetic algorithm is easy to fall into local extremum, separating mutation operation from crossover operation, and improving crossover and mutation operations. According to the energy attenuation characteristic of the underwater acoustic communication between the USV and the seabed-based, the communication area of each seabed-based is calculated, then makes further communication area optimization for preliminary optimized path. The simulation results verify that the path of further optimization is obviously better than the preliminary optimized path.

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.

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.

Particle swarm optimization with orientation angle-based grouping for practical unmanned surface vehicle path planning

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.

Unmanned surface vehicle (USV) formation is a hot topic of current research. Path planning is the core technology for USV formation. This paper focuses on a USV formation path planning problem considering kinetic constraints. Firstly, an improved A* algorithm is proposed to solve the point-to-point path planning of a USV considering kinetic constraints. In this algorithm, the yaw constraint is introduced on top of the position constraint to extend the state space of the USV to three dimensions, and the convergence speed is accelerated by building a heuristic map. The dynamics model of the USV is used to generate the minimum trajectory elements to ensure that the path conforms to the kinetic constraints. Secondly, the mathematical model of USV formation based on the virtual structure method is established, and the path planning scheme of formation navigation and formation reconfiguration is given according to the improved A* algorithm. Finally, we carry out a USV model identification experiment for SL900 USV and simulation experiments based on the model. The experimental results show that the output path of the proposed method is smoother compared with the traditional method. This method can provide a globally safe path with kinetic constraints for USV formation navigation and formation reconstruction.

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.

In recent years, with the continuous advancement of the construction of the Yangtze River's intelligent waterway system, unmanned surface vehicles have been increasingly used in the river's inland waterways. This article proposes a hybrid path planning method that combines an improved A* algorithm with an improved model predictive control algorithm for the autonomous navigation of the "Jinghai-I" unmanned surface vehicle in inland rivers. To ensure global optimization, the heuristic function was refined in the A* algorithm. Additionally, constraints such as channel boundaries and courses were added to the cost function of A* and the planned path was smoothed to meet the collision avoidance regulations for inland rivers. The model predictive control algorithm incorporated a new path-deviation cost while imposing a cost constraint on the yaw angle, significantly minimizing the path-tracking error. Furthermore, the improved model predictive control algorithm took into account the requirements of rules in the cost function and adopted different collision avoidance parameters for different encounter scenarios, improving the rationality of local path planning. Finally, the proposed algorithm's effectiveness was verified through simulation experiments that closely approximated real-world navigation conditions.

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.

Path planning on satellite images for unmanned surface vehicles

Aiming at the problem that the path search rules in the traditional path planning methods are divorced from the actual maneuverability of an unmanned surface vehicle (USV), a motion planning method of state prediction rapidly exploring random tree (spRRT) is proposed. This method retains the discrete search of the original rules of RRT while adding the continuity of the motion of USV. Firstly, the state information for each movement (position, yaw angle, velocity, etc.), is calculated based on the mathematical model of USV’s motion which takes into account the complete dynamic constraints. Secondly, this information is added to the RRT path search rules to predict the state points that can be reached by the USV. Furthermore, in order to improve search efficiency and reduce cost, spRRT is enhanced by an elliptic sampling domain (spRRT-Informed). The simulation results indicate that spRRT can effectively plan smooth paths for smoothly navigating USV. The inclusion of the USV motion model has improved steering performance by an average of over 40%. Additionally, the spRRT-Informed enhanced with sampling optimization strategy improves performance by at least 10% over spRRT in terms of sailing time and distance of the path. The results of the simulation conducted in a realistic scenario validate that spRRT-Informed can be used as a reference for practical applications.

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.

USV path planning algorithm based on plant growth

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.