Unmanned Surface Vehicle Research Articles

Improved DDPG algorithm-based path planning for unmanned surface vehicles

As a promising mode of water transportation, unmanned surface vehicles (USVs) are used in various fields owing to their small size, high flexibility, favorable price, and other advantages. Traditional navigation algorithms are affected by various path planning issues. To address the limitations of the traditional deep deterministic policy gradient (DDPG) algorithm, namely slow convergence speed and sparse reward and punishment functions, we proposed an improved DDPG algorithm for USV path planning. First, the principle and workflow of the DDPG deep reinforcement learning (DRL) algorithm are described. Second, the improved method (based on the USVs kinematic model) is proposed, and a continuous state and action space is designed. The reward and punishment function are improved, and the principle of collision avoidance at sea is introduced. Dynamic region restriction is added, distant obstacles in the state space are ignored, and the nearby obstacles are observed to reduce the number of algorithm iterations and save computational resources. The introduction of a multi-intelligence approach combined with a prioritized experience replay mechanism accelerates algorithm convergence, thereby increasing the efficiency and robustness of training. Finally, through a combination of theory and simulation, the DDPG DRL is explored for USV obstacle avoidance and optimal path planning.

A Monocular Ranging Method for Ship Targets Based on Unmanned Surface Vessels in a Shaking Environment

Aiming to address errors in the estimation of the position and attitude of an unmanned vessel, especially during vibration, where the rapid loss of feature point information hinders continuous attitude estimation and global trajectory mapping, this paper improves the monocular ORB-SLAM framework based on the characteristics of the marine environment. In general, we extract the location area of the artificial sea target in the video, build a virtual feature set for it, and filter the background features. When shaking occurs, GNSS information is combined and the target feature set is used to complete the map reconstruction task. Specifically, firstly, the sea target area of interest is detected by YOLOv5, and the feature extraction and matching method is optimized in the front-end tracking stage to adapt to the sea environment. In the key frame selection and local map optimization stage, the characteristics of the feature set are improved to further improve the positioning accuracy, to provide more accurate position and attitude information about the unmanned platform. We use GNSS information to provide the scale and world coordinates for the map. Finally, the target distance is measured by the beam ranging method. In this paper, marine unmanned platform data, GNSS, and AIS position data are autonomously collected, and experiments are carried out using the proposed marine ranging system. Experimental results show that the maximum measurement error of this method is 9.2%, and the average error is 4.7%.

Correlation of Seismic Data and Sediment Cores From the Seabed in the Medieval Harbour at Avaldsnes, Norway

ABSTRACTThis interdisciplinary study demonstrates the successful integration of non‐destructive geophysical methods and sediment core analyses for mapping the seabed in the historical harbour at Avaldsnes in south‐west Norway. In the Medieval Period, this was a royal manor, with the harbour as a central hub for the Hanseatic League in the 14th to 15th centuries. The use of an Autonomous Surface Vehicle (ASV) mounted with two distinct sub‐bottom profiler (SBP) systems facilitated the acquisition of high‐resolution seismic data, providing efficient imaging of the seafloor and subsurface layers. Sequence of fifteen sediment cores allowed for the description and dating of stratigraphy and the depositional environment. In combination, these methods provided a multidimensional understanding of the distribution of subsea sediments in the harbour, while preserving most of the archaeological context. The analysis revealed organic‐rich sediments, primarily consisting of fine detritus gyttja, accumulated over the last 1700 years, trapping certain archaeological features. Deposition of the soft sediments likely commenced due to a lower relative sea level, possibly accompanied by intensified land‐use and harbour activity. The study identified several minor anomalies in the seismic data, potentially representing artefacts of archaeological importance. An anomaly of significant size might correspond to a buried shipwreck or harbour structure. By providing a comprehensive understanding of sediment dynamics and human‐environment interactions in the harbour, the research contributes to the preservation and management of an important cultural heritage site.

Adaptive Fuzzy Optimal Control of Multiple Autonomous Surface Vehicles with Uncertain Dynamics

Adaptive Fuzzy Optimal Control of Multiple Autonomous Surface Vehicles with Uncertain Dynamics

Human errors analysis for remotely controlled ships during collision avoidance

To address human errors in collision avoidance tasks of remotely controlled ships, this study aims to develop a comprehensive framework for human error analysis within the context of autonomous ships. Firstly, the Hierarchical Task Analysis method is utilized to identify crew collision avoidance tasks associated with the traditional ship, and these tasks are then dissected into different operational stages using the Information Decision Action in a Crew cognitive model. Secondly, a combination of the fault hypothesis method and expert opinions are used to identify potential human error that may occur during collision avoidance operations of remotely controlled ships. Thirdly, an integrated approach is proposed to build a quantitative risk assessment model, which combines Failure Mode and Effects Analysis, Evidential Reasoning, and Belief rules-based Bayesian Network. Then, axiomatic analysis is used to verify the robustness and applicability of the risk assessment model. Finally, based on the results of quantitative risk assessment, specific measures are proposed for enhancing the safety of collision avoidance process of remotely controlled ships. The findings show that uncoordinated interactions of human-computer systems during the decision-making stage are a pivotal factor in the collision avoidance process. Therefore, future design efforts for remote-control centre should prioritize improving the clarity of human-computer interaction interfaces.

Path planning for submersible surface ships in a three-dimensional environment considering safety distance

Currently, path planning for an unmanned ship is usually performed in a single domain on the water surface or underwater, and little research exists on path planning for a three-dimensional (3D) environment where the ship crosses the water’s surface and underwater. The existing 3D path planning research still has improvement possibilities in reserving a safe distance between the ship and the obstacles. In this paper, path planning for a submersible surface ship (SSS) in a 3D environment is carried out based on an ant colony algorithm. Path safety is enhanced by introducing constraints related to boat width to keep the path at a safe distance from obstacles. By extracting key points, the length of the path is further reduced. The algorithmic procedure with the objective of the shortest time was obtained by assigning different speeds to the SSS sailing at the same depth and across different depths. The rationality and superiority of the method given in this paper are verified in several sets of simulations and comparative experiments. The paper concludes that path selection is more inclined to choose the water surface as the depth of the obstacles increases and the density of the obstacles decreases.

Prescribed-Time Dynamic Positioning Control for USV with Lumped Disturbances, Thruster Saturation and Prescribed Performance Constraints

This work studies the dynamic positioning (DP) control issue of unmanned surface vessels subjected to thruster saturation, error constraints, and lumped disturbances composed of time-varying marine environmental disturbances and model parameter uncertainties. Combining the disturbance-accurate estimation technique and the prescribed performance control strategy, a novel prescribed-time DP control scheme is established to address this challenging problem. In particular, the prescribed-time lumped disturbance observer is designed to accurately estimate external marine disturbances, which guarantees that the estimation error converges to zero within a prescribed time. Subsequently, a prescribed performance control strategy is proposed to guarantee that the positioning errors of DP surface vessels with thruster saturation constraints meet the error constraints requirements within a prescribed time. Furthermore, an anti-windup compensator is presented to mitigate the thruster saturation and improve the robustness of the DP control system. The stability analysis demonstrates that all positioning errors of the closed-loop system can converge to predefined performance constraints within a prescribed time. Finally, the numerical simulation confirms the efficacy and superiority of the proposed PTDP scheme.

Deep reinforcement learning for collision avoidance of autonomous ships in inland river

Due to the characteristics of large inertia, large time delay, and nonlinearity in ship motion, the maneuverability of ships is an important issue related to the safety of ship navigation. The manipulation of ships significantly affects the safety of ship navigation, and its importance will gradually increase with the development of autonomous ships. Collision accidents are common due to the complexity of inland waterways and density of the ships. Herein, an autonomous learning framework with deep reinforcement learning (DRL) is constructed for autonomous driving tasks. The state space, action space, reward function and neural network structure of DRL are designed based on the ship’s maneuvering characteristics and control requirements. A DDPG algorithm is then used to implement the controller. Finally, some representative route segments of the inland waterway is selected for simulation research based on the virtual simulation environment. The designed DRL controller can quickly converge from the training and learning process to meet the control requirements. By comparing with the previous experimental results, the effectiveness of this algorithm is verified. Therefore, this study provides a reference for future research on the collision avoidance technology of autonomous ships in inland rivers.

An In-Depth Analysis of Collision Avoidance Path Planning Algorithms in Autonomous Vehicles

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.

Design of A Self-Correctable Docking Mechanism in Disturbed Water Surface Environment

The docking mechanism is critical for unmanned surface vehicle (USV) applications, such as traction, dragging, charging, data transmission, building pontoons, etc. These applications are significant for observation, exploration, data acquisition, patrol, rescuing, etc., in aquatic environments. In this paper, a novel autonomous docking mechanism for USVs is proposed. It is capable of (1) docking the USV subject to disturbance efficiently, (2) latching without power consumption after the docking is completed, and (3) guiding the docking head to three specific angles at the end of the docking process. This paper proved the feasibility and rationality of the docking mechanism through theoretical force analysis. Experiments with three different scenarios are implemented to test the mechanism's performance. The experimental results show that the docking success rate is greater than or equal to 90 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\%$</tex-math></inline-formula> on the disturbed water surface.

An intelligence enhancement method for USV navigation visual measurement based on variable gradient soft-threshold correction

Maritime image enhancement is crucial in marine engineering for applications like unmanned surface vessels (USV) visual measurement and path planning. This paper proposes an image enhancement algorithm based on variable gradient soft-threshold correction (VGSC), tailored for USV intelligent navigation. By calculating the gradient of the two-dimensional difference quotient function of the pixels, we determine the pixel distribution and threshold. An innovative dynamic equalization function is applied to compress maximum pixel values and stretch minimum ones, allowing for adaptive adjustment of the threshold. The results indicate that the VGSC algorithm effectively suppresses background noise, thereby facilitating the extraction of target contour pixel information. When evaluated using four image assessment indicators, the VGSC algorithm enhances the image quality by 20.09 % and 28.92 % compared to bilateral filtering and Gamma correction, respectively. This method excels in enhancing image details and suppressing noise, promising robust application in multi-modal data fusion and precise maritime target detection.

Fully Distributed Dynamic Event-triggered Formation-Containment Control for Networked Unmanned Surface Vehicles with Intermittent Wireless Network Communications

Fully Distributed Dynamic Event-triggered Formation-Containment Control for Networked Unmanned Surface Vehicles with Intermittent Wireless Network Communications

Path following control of under-actuated autonomous surface vehicle based on random motion trajectory dataset and offline reinforcement learning

Path following control of under-actuated autonomous surface vehicle based on random motion trajectory dataset and offline reinforcement learning

Research on Navigation Risk Assessment of Unmanned Ship Under Complex Navigation Conditions

With the research on unmanned ships conducted by the International Maritime Organization, the European Union and China, the navigation risk of unmanned ships has become a hot topic around the world. Based on the research and development history and current situation of unmanned ships at home and abroad, focusing on the three key elements of “Unmanned Ship, shore-based control personnel, external navigation environment”, this study establishes a navigation risk index system for unmanned ships under complex navigation conditions, considering the particularity of the unmanned ship, its perception ability, environmental understanding, situation judgment, communication ability and other indicators. The analytic hierarchy process is used to solve the weight of all kinds of navigation risk factors, and a consistency test of the established index system is carried out. Through expert investigation, the fuzzy membership set of risk indexes is established, and a fuzzy comprehensive evaluation model is established to evaluate the navigation risk of unmanned ships under complex navigation conditions. To avoid the situation of single-factor information being inundated, this study adopts the method of fuzzy comprehensive evaluation from a low level to a high level, and it verifies the algorithm through cases, which proves the validity and rationality of the proposed risk assessment method.

Investigation on the Optimization Strategy of DWA Algorithm for Path Planning of the USVs with the Consideration of Environmental Factors

Unmanned surface vessels (USVs) are generally subjected to wind force, wave and undercurrent action. Some unidentified objects such as underwater obstacles and submerged reefs should be monitored and evaded with converged communication, autonomous control and network system so as to achieve autonomous and automatic avoidance of danger and real-time control of flight path. Next, based on performing interference mechanics analysis on the USVs under the disturbance of wind force and wave on water surface, the relationship parameters between the bow angle, the deviation angle, safety evaluation function and the external force were obtained, and the comprehensive information of water surface environment was evaluated in combination with the dynamic window approach (DWA) algorithm. Further, the related model was established for simulation by using the simulation software MATLAB, and both reliability and stability of the modified DWA algorithm were designed and examined. Accordingly, the modified DWA algorithm can enhance the safety of the USVs in autonomous path planning and achieve rapid obstacle avoidance and autonomous path planning of the USVs. Our simulation revealed that the modified DWA algorithm can safely and rapidly correct the bow angle and the deviation angle, optimize the path trajectory and reduce the risk of roll-over of the USVs under sudden external force.

Modeling and solving time-sensitive task allocation for USVs with mixed capabilities

Efficient task allocation for Unmanned Surface Vehicles (USVs) in maritime operations is crucial for optimal resource utilization and mission success. This paper introduces a task assignment model that accounts for the capabilities and time constraints associated with the heterogeneity of USVs. An Extended-Restriction Multiple Traveling Salesmen Problem (ER-MTSP) model is formulated, encapsulating the diverse capabilities of USVs and time restrictions. To solve this problem, a Fuzzy Enhanced Non-Dominated Sorting Genetic Algorithm (FENSGA-II) is developed, incorporating adaptive random testing initialization and customized hierarchical operators to generate high-quality Pareto frontiers. The fuzzy-linguistic satisfactory degree then re-evaluates the linguistic importance preferences of the objectives within the Pareto set, aiding in reasonable decision-making. Additionally, a two-phase refinement strategy balances individual task values and structure topology, enabling flexible task selection in emergency situations. Simulations and lake trials conducted across various problem variants demonstrate the effectiveness of the model. The results highlight the superiority of our approach compared to current combinatorial optimization methods, providing enhanced solutions across different problem variations.

Development of an Autonomous Ship Navigation System Based on the A*-AHP Hybrid Path Planning Method

Development of an Autonomous Ship Navigation System Based on the A*-AHP Hybrid Path Planning Method

Cooperative Containment Strategies to Pursue One Faster Evader with Collision Avoidance

On the surface of complex sea areas with obstacles, the encirclement mission against one hostile unmanned surface vehicle (USV) with high capability is usually extremely challenging. Especially when the evasion USV is endowed with a higher speed, the pursuit USVs are more likely to fail. In this work, the containment strategies by multiple low-speed pursuit USVs to capture one high-speed evasion USV considering collision avoidance are studied. On the basis of the conventional artificial potential field (APF) method, we first design a virtual target point to replace the actual evasion USV, which will help the pursuit USVs better implement the surrounding behavior. Thereafter, we reset the triggering conditions of the APF method to avoid the unnecessary obstacle avoidance actions. The proposed strategies can help the pursuit USVs better accomplish the containment strategies while bypassing the obstacles. Simulation experiments have verified the effectiveness of the designed cooperative containment strategies.

Dynamic Path Planning Method for Unmanned Surface Vessels in Complex Traffic Conditions of Island Reefs Waters

Unmanned Surface Vehicles (USVs) operating in complex traffic conditions in island reef waters often require different types of algorithms. Therefore, selecting a dynamic path-planning algorithm with strong adaptability has become a new challenge. This paper proposes a dynamic adaptive path planning algorithm for USVs, incorporating an improved Dynamic Window Approach (DWA) with fuzzy logic and the International Regulations for Preventing Collisions at Sea (COLREGS). The algorithm is designed by integrating three key aspects: evaluation function, fuzzy control, and COLREGS. First, to enable USVs to approach the target point more safely and quickly during navigation, an additional target point attraction sub-function is introduced, extending the original evaluation function. Furthermore, to ensure robust dynamic path planning for USVs across various water environments, such as narrow channels, reef-laden waters, and open seas, fuzzy logic is integrated with the improved DWA algorithm. Since USVs must comply with COLREGS during navigation, the algorithm incorporates these regulations, enhancing the DWA algorithm with fuzzy logic to ensure compliance. Finally, simulation experiments validate the proposed algorithm, demonstrating that the planned paths are safer and more stable, ensuring the safe navigation of USVs in compliance with COLREGS.

Deep Reinforcement Learning-enhanced Target Encirclement of Multiple Unmanned Surface Vessels

Deep Reinforcement Learning-enhanced Target Encirclement of Multiple Unmanned Surface Vessels