An autonomous dynamic collision avoidance control method for unmanned surface vehicle in unknown ocean environment

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.

Robust detection and recognition of water surfaces are critical for autonomous navigation of unmanned surface vehicles (USVs), since any none-water region is likely an obstacle posing a potential danger to the sailing vehicle. A novel water region visual detection method is proposed in this paper. First, the input image pixels are clustered into different regions and each pixel is assigned a label tag and a confidence value by adaptive multistage segmentation algorithm. Then the resulting label map and associated confidence map are fed into a convolutional neural network (CNN) as training samples to train the network online. Finally, the online trained CNN is used to segment the input image again but with greater precision and stronger robustness. Compared with other deep-learning image segmentation algorithms, the proposed method has two advantages. Firstly, it dispenses with the need of manual labeling training samples which is a costly and painful task. Secondly, it allows real-time online training for CNN, making the network adaptive to the navigational environment. Another contribution of this work relates to the training process of neuro network. An effective network training method is designed to learn from the imperfect training data. We present the experiments in the lake with a various scene and demonstrate that our proposed method could be applied to recognize the water region in the unknown navigation environment automatically.

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.

The reliable obstacle detection is a challenging task in autonomous navigation of unmanned surface vehicles. In this paper, we present a novel real-time obstacles detection based on monocular vision which can effectively tell apart obstacles on the sea surface from complex background. The main innovation of this paper is to propose a water-boundary-line algorithm based on semantic segmentation and random sample consistency line fitting. And use a simple and effective saliency detection method based on background prior and foreground prior to detect obstacles under the water-boundary-line. Our method can efficiently and quickly obtain obstacle information from images captured by shipborne cameras, and it has the ability to process more than 33 frames/s.

Object tracking is an important basis for the autonomous navigation of unmanned surface vehicles. However, several problems still must be addressed for a wide applicating of object tracking in unmanned surface vehicles. First, if multiple objects of the same classification exist in the same field of view, then stable extraction of an object is difficult. Second, in an environment with a complex background and large changes in object shape, the tracking accuracy is low, and object tracking errors and tracking loss can easily occur. Third, much time is required to detect a high-resolution real-time video stream, not meeting the delay requirement of the photoelectric servo stable tracking. To resolve these problems, this paper proposes an object detection-tracking algorithm based on a radar-photoelectric system. The algorithm combines an object detection algorithm with an object tracking algorithm and involves the following steps. First, a first-frame object extraction algorithm is used to extract the tracking object from the first frame. Second, a region of interest (ROI)-prediction algorithm is used to predict ROIs and detect objects in these ROIs. This algorithm can effectively solve the above problems in marine tests. When multiple objects of the same classification exist in the same field of view, the algorithm can extract the radar-guided object stably. When faced with a complex background and a large change in object shape, the algorithm substantially improves the accuracy and robustness of object tracking. Compared with the conventional object detection algorithm, the time consumption of this algorithm is reduced by 25.8%.

SummaryEffective and intelligent path planning algorithms designed for operation in a dynamic marine environment are essential for the safe operation of unmanned surface vehicles (USVs). Most of the current research deals with the ‘dynamic problem’ by basing solutions on the nonpractical assumption that each USV has a robust communication channel to obtain essential information such as position and velocity of marine vehicles. In this paper, a Kalman filter‐based predictive path planning algorithm is proposed. The algorithm has been designed to predict the trajectories of moving ships, and the USV's own position in real time and accordingly assesses collision risk. For path planning, a weighted fast marching square method is proposed and developed to search for the optimal path. The path can be optimised for mission requirements such as minimum distance to travel and the most safety path by adjusting weighting parameters. The proposed algorithm has been validated using a number of simulations that include practical environmental aspects. The results show that the algorithms can sufficiently deal with complex traffic environments and that the generated practical path is suited for both unmanned and manned vessels. Copyright © 2015 John Wiley & Sons, Ltd.

Parallel manipulators with less than six degrees of freedom (DOF) have been increasingly used in high-speed hybrid machine tools. The structural features of parallel manipulators are dynamic, a characteristic that is particularly significant when these manipulators are used in high-speed machine tools. However, normal kinematic control method cannot satisfy the requirements of the control system. Many researchers use model-based dynamic control methods, such as the dynamic feedforward control method. However, these methods are rarely used in hybrid machine tools because of the complex dynamic model of the parallel manipulator. In order to study the dynamic control method of parallel manipulators, the dynamic feedforward control method is used in the dynamic control system of a 3-PSP (prismatic-spherical-prismatic) 3-DOF spatial parallel manipulator used as a spindle head in a high-speed hybrid machine tool. Using kinematic analysis as basis and the Newton-Euler method, we derive the dynamic model of the parallel manipulator. Furthermore, a model-based dynamic feedforward control system consisting of both kinematic control and dynamic control subsystems is established. The dynamic control subsystem consists of two modules. One is used to eliminate the influence of the dynamic characteristics of high-speed movement, and the other is used to eliminate the dynamic disturbances in the milling process. Finally, the simulation model of the dynamic feedforward control system of the 3-PSP parallel manipulator is constructed in Matlab/Simulink. The simulations of the control system eliminating the influence of the dynamic characteristics and dynamic disturbances are conducted. A comparative study between the simulations and the normal kinematic control method is also presented.The simulations prove that the dynamic feedforward control method effectively eliminates the influence of the dynamic disturbances and dynamic characteristics of the parallel manipulator on high-speed machine tools, and significantly improves the trajectory accuracy. This is the first attempt to introduce the dynamic feedfordward control method into the 3-PSP spatial parallel manipulator whose dynamic model is complex and provides a study basis for the real-time dynamic control of the high-speed hybrid machine tools.

Water segmentation is essential for autonomous navigation of Unmanned Surface Vehicles (USV) in inland waterways. However, most of the existing methods are suitable for maritime environments. Due to their poor real-time performance and a high false-positive rate of water surface reflection and water-sky interference in inland waterways, we propose a radarassisted real-time water segmentation network (RaRWS) to solve the above problems. We obtain pseudo-waterlines based on radar data by deleting points, fitting them multiple times, and generating a radar mask. While simplifying the encoder backbone network, we use the Attention Refinement Module (ARM) to fuse radar masks to improve detection and waterside accuracy. In addition, a Feature Fusion Module (FFM) is introduced to help the decoder fuse high- and low-level features and further fuse radar data. RaRWS is tested in both normal and bad weather. The results show that RaRWS can achieve higher performance compared with the current state-of-the-art methods (F <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</inf> is above 99%), while gaining real-time performance (41.3fps).

A two-stage dynamic collision avoidance algorithm for unmanned surface vehicles based on field theory and COLREGs

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

COLREGs-abiding hybrid collision avoidance algorithm based on deep reinforcement learning for USVs

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.

This article studies the trajectory tracking of an underactuated unmanned surface vehicle. The trajectory tracking controller is divided into two parts: the kinematics controller and the dynamic controller. Taking the hull coordinate system of the unmanned surface vehicle as the datum plane, the kinematics controller is designed based on the backstepping method, and the virtual control input of the speed and the heading deviation of the ship are obtained. A dynamic controller is designed through an exponential sliding surface to stabilize the error of speed and heading deviation, thereby stabilizing the position error. In the process of designing the controller, the stability of the entire closed-loop control system is analyzed using Lyapunov stability theory. Finally, the designed numerical simulation experiment is carried out. The proposed exponential sliding mode controller is compared with the global integral sliding mode controller, and it is verified that the proposed controller has better effectiveness and robustness in the trajectory tracking of the underactuated unmanned surface vehicle.

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.