FTILOS-Based Path Following Control for Underactuated USVs With Obstacle Avoidance: A Planning-Guidance-Control Integrated Design

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.

To solve the problem of path tracking control for search and rescue unmanned surface vehicle(USV), an adaptive path tracking control method based on line-of-sight(LOS) navigation algorithm and fuzzy PID control is proposed. Firstly, the motion model of USV is constructed. Secondly, the path tracking problem is transformed into a heading control problem according to the LOS algorithm. Then, a fuzzy adaptive PID path tracking controller based on error-driven is designed, which can achieve real-time optimization of PID parameters. Finally, the control effect of the fuzzy PID controller is verified by taking the expanded square search and parallel line search as examples. The simulation results show that the fuzzy PID controller has better adaptability and robustness than the classical PID controller. And it has better control effect under different paths.

To achieve real-time obstacle avoidance for underwater robots, the artificial potential field method can be selected as the obstacle avoidance path planning algorithm. However, this method applies a large attractive force to the underwater robot when it is far from the target point, causing it to collide with obstacles. Additionally, when the underwater robot just enters the repulsion influence zone, the repulsion force that it experiences undergoes an abrupt change, resulting in a non-smooth obstacle avoidance path. Furthermore, developing the obstacle avoidance function based on software platforms such as MATLAB has a long algorithm verification cycle, and it is difficult to transfer the algorithm model to practical scenarios. Therefore, this paper proposes an underwater robot obstacle avoidance method based on ROS. Firstly, a dynamic adjustment factor is introduced into the potential field function of the artificial potential field method to redefine the obstacle influence zone, and an improved artificial potential field method is designed as the obstacle avoidance path planning algorithm for underwater robots. The experiment is conducted on the ROS simulation platform, and the results show that the improved algorithm can solve the two problems existing in the artificial potential field method. Secondly, nodes required for underwater robot obstacle avoidance are established based on the ROS operating system, and the improved artificial potential field method is integrated into the obstacle avoidance path planning node. Finally, the obstacle avoidance experiment of the Bluerov2 underwater robot is conducted in a pool, and the experimental results demonstrate that the proposed method can achieve real-time obstacle avoidance for underwater robots.

The use of autonomous marine robots has been rapidly increasing in the development of marine environments. This paper presents the development of an autonomous surface vehicle (ASV) and validation of autonomous navigation technologies. The ASV was developed as a testbed to validate fundamental navigation technologies for autonomous marine robots including both surface and underwater vehicles. The ASV was developed on a catamaran by integrating various sensors and power and electrical propulsion systems, navigation and control systems, and communication devices. As fundamental navigation technologies for ASV, waypoint tracking and obstacle avoidance are developed and tested. Further, using the ASV as a testbed to validate underwater navigation technologies, two types of underwater localization methods were developed. First, an acoustic-based navigation was developed by using acoustic sources as landmarks in underwater environment. The proposed acoustic-based navigation localized both vehicle and source by Kalman filter-based SLAM. Second, terrain-based localization as geophysics-based navigation was developed using a particle filter. Effective resampling can be achieved by using a measurement of terrain roughness. Both acoustic and terrain-based underwater navigation methods are implemented with the developed ASV, and the performance of the navigation methods are evaluated by comparing the estimated localization results to the ground truth acquired by GPS. Several field tests conducted in inland water environments demonstrate the performance of the developed vehicle and navigation algorithms.

Advanced countries around the world are spurring the development of Unmanned Surface Vehicles (USVs) that can operate autonomously at marine environment. The key enabling technology for such USVs is the mission planning system (MPS) that can autonomously navigate through the harsh waters. The MPS not only has the functions for the navigation, but also has the capabilities, such as obstacle avoidance, malfunction corrections, dealing with unexpected events, return home functions, and many other eventualities that cannot be programmed in advance. The autonomy levels are increasingly moving higher and it is foreseeable that the trend will continue in the future. The main purpose of this paper is the analysis of the MPS onboard the USVs, in terms of the categories, functions, and technological details. Also, we analyze the case study of autonomous mission planning control systems in various fields and introduce the features that constitute the critical functionalities of the mission planning systems.

A COLREGs-compliant guidance strategy for an underactuated unmanned surface vehicle combining potential field with grid map

Surface-vehicle berthing is a complex and challenging task. It involves complicated ship dynamics owing to a large drift angle and the time-varying environmental disturbances induced by wind, wave, and current. Therefore, berthing requires state processing at each step and a controller that can respond to the non-linear behavior of the unmanned surface vehicle (USV). Herein, a systematic approach for the autonomous berthing of a USV is proposed. A state-machine approach is proposed to solve state transitions in the berthing step. A model reference adaptive controller was adopted to cope with the uncertainty of USV dynamics during berthing. Herein, the theoretical background and design of the adaptive controller are described. Simulation for the validation of the proposed method was conducted using the robot operating system Gazebo-based simulator. Finally, the respective performances of the proposed method and conventional controller were compared. Using the proposed berthing approach, an accurate and stable docking of small USVs became achievable.

In recent years, ZigBee has been proven to be an excellent solution to create scalable and flexible home automation networks. In a home automation network, consumer devices typically collect data from a home monitoring environment and then transmit the data to an end user through multi-hop communication without the need for any human intervention. However, due to the presence of typical obstacles in a home environment, error-free reception may not be possible, particularly for power constrained devices. A mobile sink based data transmission scheme can be one solution but obstacles create significant complexities for the sink movement path determination process. Therefore, an obstacle avoidance data routing scheme is of vital importance to the design of an efficient home automation system. This paper presents a mobile sink based obstacle avoidance routing scheme for a home monitoring system. The mobile sink collects data by traversing through the obstacle avoidance path. Through ZigBee based hardware implementation and verification, the proposed scheme successfully transmits data through the obstacle avoidance path to improve network performance in terms of life span, energy consumption and reliability. The application of this work can be applied to a wide range of intelligent pervasive consumer products and services including robotic vacuum cleaners and personal security robots.

USV path planning algorithm based on plant growth

This study explores the challenge of formation control for unmanned surface vehicles (USVs) with dynamic uncertainties and external disturbances. A hierarchical fixed-time control (HFTC) framework is designed to solve the estimation problem and the formation tracking problem for USVs. The HFTC framework consists of estimators based on the impulsive control, which help the USVs in the network obtain states of leader accurately, and local controllers with fixed-time extended state observer (FTESO), which drive the USVs achieve predefined configuration in fixed time. Through systematic analysis, sufficient conditions are given for guaranteeing the stability and convergence of the studied closed-loop system. Ultimately, this paper presents simulation results to validate the effectiveness of the implemented control scheme.

With the development of computer technology, the intelligent level of vessel continues to improve, and vessel' autonomous obstacle avoidance navigation has gradually become a research hotspot. In order to solve the problem of autonomous obstacle avoidance navigation of surface unmanned vessel in complex restricted waters, a method of obstacle avoidance route planning based on multi-objective evolutionary algorithm is proposed. First, a multi-objective route planning model with the goal of minimizing route distance and navigation risk is established. At the same time, angle constraints are set in the model to reduce unnecessary steering of the route and improve the actual maneuverability of the planned route. Finally, NSGA-II in the multi-objective evolutionary algorithm is introduced to solve the route planning model and realize obstacle avoidance route planning. Simulation experiments and comparison with other traditional algorithms prove that this method can quickly and effectively design obstacle avoidance routes, reduce ship navigation risks, and realize intelligent route planning in complex restricted waters.

The local path planning of unmanned ships has high real-time requirements, and the main research methods such as dynamic window method, random tree method, artificial potential field method, ant colony algorithm, particle swarm algorithm and other methods are difficult to be directly applied to path planning. Aiming at the local collision avoidance problem of unmanned ships, this paper proposes a local collision avoidance method for unmanned ships based on particle swarm optimization algorithm. The algorithm introduces fuzzy mathematics theory to judge the collision risk of unmanned boats and obstacles, transforms the local obstacle avoidance problem of unmanned boats into the optimization problem of the speed and heading changes of small boats, and integrates maritime rules and unmanned boat dynamics. The constraint conditions further optimize the value range of the speed and heading, so that the optimal obstacle avoidance strategy can be calculated through the optimization of the particle swarm optimization algorithm. The simulation research simulates the complex environment at sea and selects typical encounter situations in the maritime rules for testing. The simulation results show that the proposed method can well adapt to obstacle avoidance situations such as ship encounters, overtaking, and cross encounters.

Unmanned surface vessels (USVs) are supposed to be able to adapt unstructured environments by means of multi-sensor active perception without any human interference, and high-accuracy path following is achieved for USVs by effective control strategies and intelligent devices of e-navigation. This paper proposes a finite-time predictor line-of-sight (LOS)-based integral sliding-mode adaptive neural (FPISAN) scheme for the path following of USVs in the presence of unknown dynamics and external disturbances, which copies with the problem of merging with the kinematic level and the kinetic level of USVs. From the point of view of USVs’ practical engineering, the inertia matrix of USVs maintains nonzero off-diagonal. In order to ensure that USVs can converge to and follow a defined path, a novel LOS-based guidance law that can acquire sideslip angles by error predictors within a finite time is presented, called finite-time predictor-based LOS (FPLOS). Then, the path-following control laws are designed by combining the neural network (NN) technique with the integral sliding-mode method, where radial basis function NN (RBFNN) is applied to approximate lumped unknown dynamics induced by nonparametric uncertainties and external disturbances. The theoretical analysis verifies that the path-following guidance-control system of USVs is semiglobally uniformly ultimately bounded (SGUUB) with the aid of Lyapunov stability theory. The effectiveness and performance of this presented scheme are illustrated by simulation experiments with the comparison. Note to Practitioners —The design of heading guidance laws and path-following control laws for path following of USVs subject to unknown dynamics and external disturbances is a critical problem, which affects the development of USVs. This problem associated with practical engineering of USVs due to the actual navigation environment that is complex, diversified, and highly unstructured. This paper presents a wholly tight strategy to compensate for unknown sideslip angles and approximate lumped unknown dynamics. Hence, an effective scheme being denoted FPISAN mentioned above is developed for path following of USVs.

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.