Autonomous obstacle avoidance of an unmanned surface vehicle based on cooperative manoeuvring

Wu, Peng; Xie, Shaorong; Liu, Hengli; Li, Ming; Li, Hengyu; Peng, Yan; Li, Xiaomao; Luo, Jun

doi:10.1108/ir-04-2016-0127

Cited by 46 publications

(18 citation statements)

References 32 publications

(30 reference statements)

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“…Combining global path planning with local path planning, the shortcut Dijkstra algorithm and modified artificial potential field (APF) algorithm were used for the vessel's navigation in [5]. After, [6] improved the global path planning, that is the artificial potential field-ant colony optimization (APF-ACO) obstacle-avoidance algorithm. Based on the Voronoi diagram and Fermat's spiral, [7] produced the path in real time, which steered the vessel course by an indirect adaptive line-of-sight (LOS) guidance algorithm.…”

Section: Introductionmentioning

confidence: 99%

“…Kuwata et al [9] completed the task of safe maritime autonomous navigation by using the velocity obstacles (VO) method. The environmental information of the works in [3][4][5][6] was obtained from satellite images, and that of [8,9] from cameras or sonar. As previously mentioned, although many papers choose satellite images, some invisible information, such as reefs, ocean depth, and so on, does not exist.…”

Section: Introductionmentioning

confidence: 99%

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Collision Avoidance Using Finite Control Set Model Predictive Control for Unmanned Surface Vehicle

Sun¹,

Wang²,

Fan³

et al. 2018

Applied Sciences

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In recent years, with the development of unmanned platforms, unmanned surface vehicles (USV) are attracting more and more attention. Compared to ordinary ships, USV have a smaller volume and faster speed, so their collision avoidance system (CAS) should have better responsiveness and stability. The paper describes a method that is based on finite control set model predictive control (FCS-MPC). A finite control set is generated by more practical control commands: the thruster speed and propulsion angle of the USV. The method is conceptually and computationally simple and yet quite versatile, as it can account for the dynamics of the USV, steering and propulsion system. Based on the theory of FCS-MPC, a safe and fast CAS is proposed, and it is verified in different static and dynamic environments. The real environment model for collision avoidance is established by extracting the environment data from the electronic chart. The result shows that the method is effective and can control the USV to sail safely and quickly in complex real scenarios with multiple dynamic obstacles.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Collision Avoidance Using Finite Control Set Model Predictive Control for Unmanned Surface Vehicle

Sun¹,

Wang²,

Fan³

et al. 2018

Applied Sciences

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“…Although these algorithms have good real-time performance, they are difficult to use in complex and irregular environments. To solve this problem, some scholars use the hierarchical structure to combine global path planning and local path re-planning [6,11,12], and some try to improve the traditional path planning algorithm, using for example Rule-based Repairing A* [8]. Liu et al selected the fast marching method (FMM)-based path planning algorithm [20].…”

Section: Introductionmentioning

confidence: 99%

“…Hence, path re-planning (collision avoidance) is proposed to meet the demands of real-time planning or avoidance under dynamic environments. These algorithms include the rolling windows method [11], artificial potential field (APF) [6,12], velocity obstacle (VO) [15,16], local reactive obstacle avoidance [17], optimal reciprocal collision avoidance [18], dynamical virtual ship (DVS) [19], and so on. Although these algorithms have good real-time performance, they are difficult to use in complex and irregular environments.…”

Section: Introductionmentioning

confidence: 99%

“…These include Dijkstra's algorithm [6], A* [7,8], Theta* [9], the Voronoi diagram [10], particle swarm optimization (PSO) [11], ant colony optimization (ACO) [12,13], the genetic algorithm (GA) [14], and so on. Recently, considering the turning capacity of USVs, Yang and Tseng used the finite Angle A* algorithm (FAA*), which is used to determine safer and suboptimal paths for USVs on satellite thermal images [7].…”

Section: Introductionmentioning

confidence: 99%

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An Automatic Navigation System for Unmanned Surface Vehicles in Realistic Sea Environments

et al. 2018

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Abstract:In recent years, unmanned surface vehicles (USVs) have received notable attention because of their many advantages in civilian and military applications. To improve the autonomy of USVs, this paper describes a complete automatic navigation system (ANS) with a path planning subsystem (PPS) and collision avoidance subsystem (CAS). The PPS based on the dynamic domain tunable fast marching square (DTFMS) method is able to build an environment model from a real electronic chart, where both static and dynamic obstacles are well represented. By adjusting the Saturation, the generated path can be changed according to the requirements for security and path length. Then it is used as a guidance trajectory for the CAS through a dynamic target point. In the CAS, according to finite control set model predictive control (FCS-MPC) theory, a collision avoidance control algorithm is developed to track trajectory and avoid collision based on a three-degree of freedom (DOF) planar motion model of USV. Its target point and security evaluation come from the planned path and environmental model of the PPS. Moreover, the prediction trajectory of the CAS can guide changes in the dynamic domain model of the vessel itself. Finally, the system has been tested and validated using the situations of three types of encounters in a realistic sea environment.

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Unmanned surface vessel obstacle avoidance with prior knowledge‐based reward shaping

Wang

Luo

et al. 2020

Concurrency and Computation

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Autonomous obstacle avoidance control of unmanned surface vessels (USVs) in complex marine environments is always fundamental for its scientific search and detection. Traditional methods usually model USV motion and environments in a mathematical way that needs perceptual information. Unfortunately, it is difficult to provide sufficient perceptual information due to complex marine environments, resulting in inaccurate modeling. Reinforcement learning has recently enjoyed increasing popularity in the problem of obstacle avoidance since it can settle problems by partially observable environment information. However, the autonomous USV obstacle avoidance using reinforcement learning is still facing the challenge of designing appropriate reward functions under complex marine environments. To address these issues, we propose a prior knowledge‐based USV reinforcement learning obstacle avoidance algorithm. In this algorithm, an actor‐critic network is used as the main architecture of the algorithm, and prior knowledge‐based reward shaping used to design relevant reward function for USV obstacle avoidance. A standard USV based on a visual sensor is designed, and the state input of the algorithm is through USV's front vision sensor. We conducted simulation experiments and results prove that our algorithm can effectively converge, and USV achieves high velocity and low collision rate in the complex marine environment.

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Autonomous obstacle avoidance of an unmanned surface vehicle based on cooperative manoeuvring

Cited by 46 publications

References 32 publications

Collision Avoidance Using Finite Control Set Model Predictive Control for Unmanned Surface Vehicle

Collision Avoidance Using Finite Control Set Model Predictive Control for Unmanned Surface Vehicle

An Automatic Navigation System for Unmanned Surface Vehicles in Realistic Sea Environments

Unmanned surface vessel obstacle avoidance with prior knowledge‐based reward shaping

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