Unmanned Surface Vehicle Navigation Research Articles

A Two Layered Optimal Approach towards Cooperative Motion Planning of Unmanned Surface Vehicles in a Constrained Maritime Environment

Efficient motion planning of multiple unmanned surface vehicles (USVs) in a dynamic maritime environment is an important requirement for increasing mission efficiency and achieving motion goals. The current study integrates two approaches of intelligent path planning and virtual target path following guidance for multi-agent USV framework to perform a coordinated and cooperative navigation of USVs in a constrained maritime environment. In the current study, a safety distance constrained A* approach produces an optimal, computationally efficient and collision free path which is later smoothed using a spline to provide an optimal trajectory as input for virtual target based multi-agent guidance framework to navigate multiple USVs. The virtual target approach provides a robust methodology of global and local collision avoidance based on known positions of vehicles. The combined approach is evaluated with the different number of USVs and in different environmental scenarios to understand the effectiveness of approach from the perspective of practicality, safety and robustness.

Obstacle Avoidance Approaches for Autonomous Navigation of Unmanned Surface Vehicles

The adoption of a robust collision avoidance module is required to realise fully autonomous Unmanned Surface Vehicles (USVs). In this work, collision detection and path planning methods for USVs are presented. Attention is focused on the difference between local and global path planners, describing the most common techniques derived from classical graph search theory. In addition, a dedicated section is reserved for intelligent methods, such as artificial neural networks and evolutionary algorithms. Born as optimisation methods, they can learn a close-to-optimal solution without requiring large computation effort under certain constraints. Finally, the deficiencies of the existing methods are highlighted and discussed. It has been concluded that almost all the existing method do not address sea or weather conditions, or do not involve the dynamics of the vessel while defining the path. Therefore, this research area is still far from being considered fully explored.

무인수상선의 자율운항을 위한 단안 카메라 기반의 근거리 선박 자동 탐지

Automatic detection and tracking of nearby ships are important capabilities for the autonomous operation of unmanned surface vehicles (USVs). This study focuses on achieving such capabilities in the framework of vision-based perception and sensor fusion. Reliable detection and tracking processes using a monocular camera are designed to automatically detect maneuvering targets with no prior information on the motion of targets. For a reliable trajectory estimation in low observability situations, the proposed vision-based approach uses bearing information in both the horizontal and vertical directions. In addition, the measurement by an onboard lidar is integrated into the vision-based tracking filter when the targets are in close range. The performance of the proposed method was assessed by field experiment data obtained in a real-sea environment to show the feasibility of the developed algorithms for the autonomous navigation of USVs.

무인 수상정의 융합 항법 및 GPS 이상 검출

무인 수상정의 융합 항법 및 GPS 이상 검출

A novel method of unmanned surface vehicle autonomous cruise

Purpose – This paper aims to propose a new method for combining global path planning with local path planning, to provide an efficient solution for unmanned surface vehicle (USV) path planning despite the changeable environment. Path planning is the key issue of USV navigation. A lot of research works were done on the global and local path planning. However, little attention was given to combining global path planning with local path planning. Design/methodology/approach – A search of shortcut Dijkstra algorithm was used to control the USV in the global path planning. When the USV encounters unknown obstacles, it switches to our modified artificial potential field (APF) algorithm for local path planning. The combinatorial method improves the approach of USV path planning in complex environment. Findings – The method in this paper offers a solution to the issue of path planning in changeable or unchangeable environment, and was confirmed by simulations and experiments. The USV follows the global path based on the search of shortcut Dijkstra algorithm. Both USV achieves obstacle avoidances in the local region based on the modified APF algorithm after obstacle detection. Both the simulation and experimental results demonstrate that the combinatorial path planning method is more efficient in the complex environment. Originality/value – This paper proposes a new path planning method for USV in changeable environment. The proposed method is capable of efficient navigation in changeable and unchangeable environment.

Predictive navigation of unmanned surface vehicles in a dynamic maritime environment when using the fast marching method

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.

Model-Based Synthetical Optimization Analysis on Navigation Performance of Unmanned Surface Vehicle

Unmanned Surface Vehicle (USV) has very complex forces and states when sailing at sea. While the optimization of USV via regression equations usually has a lower precision. Rapidity and seakeeping tests have been conducted based on a USV model of JUST. The rapidity test is mainly ship model resistance test and the seakeeping tests include wave added resistance test, hydrostatic pitching test, pitching and heaving test under the wave. Response surfaces fitting vectors of rough water resistance, pitching significant value, heaving significant value is established while using VC++ language to write second-order response surface fitting programs after dimensionless conversion of test data. Mathematical model of USV navigation performance synthetical optimization is proposed, including systems of rapidity, maneuverability and seakeeping. A layered parallel genetic algorithm (L-P-GA) optimization program is written in VC++ language, which can optimize a variety of conditions with different design speed and displacements. The results indicate that the optimization method has a higher precision and it can provide an opportunity to optimize the navigation performance of USV effectively.

An adaptive obstacle avoidance algorithm for unmanned surface vehicle in complicated marine environments

Unmanned surface vehicles (USVs) are important autonomous marine robots that have been studied and gradually applied into practice. However, the autonomous navigation of USVs, especially the issue of obstacle avoidance in complicated marine environment, is still a fundamental problem. After studying the characteristics of the complicated marine environment, we propose a novel adaptive obstacle avoidance algorithm for USVs, based on the Sarsa on-policy reinforcement learning algorithm. The proposed algorithm is composed of local avoidance module and adaptive learning module, which are organized by the “divide and conquer” strategy-based architecture. The course angle compensation strategy is proposed to offset the disturbances from sea wind and currents. In the design of payoff value function of the learning strategy, the course deviation angle and its tendency are introduced into action rewards and penalty policies. The validity of the proposed algorithm is verified by comparative experiments of simulations and sea trials in three sea-state marine environments. The results show that the algorithm can enhance the autonomous navigation capacity of USVs in complicated marine environments.

Unified Approach of Unmanned Surface Vehicle Navigation in Presence of Waves

Most of the present work for unmanned surface vehicle (USV) navigation does not take into account environmental disturbances such as ocean waves, winds, and currents. In some scenarios, waves should be treated as special case of dynamic obstacle and can be critical to USV’s safety. For the first time, this paper presents unique concept facing this challenge by combining ocean waves' formulation with the probabilistic velocity obstacle (PVO) method for autonomous navigation. A simple navigation algorithm is presented in order to apply the method of USV’s navigation in presence of waves. A planner simulation dealing with waves and obstacles avoidance is introduced.