Angular rate-constrained path planning algorithm for unmanned surface vehicles

Kim, Hanguen; Kim, Dong-Hoon; Shin, Jae-Uk; Kim, Hyongjin; Myung, Hyun

doi:10.1016/j.oceaneng.2014.03.034

Cited by 118 publications

(59 citation statements)

References 18 publications

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“…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%

“…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]. Kim put forward the angular rate-constrained Theta* algorithm with considerations of vehicle performance based on the Theta* algorithm [9]. However, all of these algorithms search the optimal path in a static environment.…”

Section: Introductionmentioning

confidence: 99%

“…In addition, although many works in the literature (see e.g., [6,7,9]), use satellite maps for environmental information, this method is incomplete for describing the invisible sea environment, such as reefs, ocean depths, and so on. Thus in this paper, environmental information is obtained from the electronic chart, which can display this invisible information in detail.…”

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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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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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“…It is the first work specifically solving the USV formation problem with algorithm practicability as the main feature of this research. A number of previous works have developed path planning algorithms for USVs; however, nearly all of them (Tam and Bucknall, 2013;Naeem et al, 2012;Thakur et al, 2012), with the notable exception of Kim et al (2014), simulated algorithms in simple self-constructed environments rather than real ocean environments. The algorithm designed in this paper is able to extract information from a real navigation map to construct a synthetic grid map, where both static and dynamic obstacles are well represented.…”

Section: Introductionmentioning

confidence: 99%

Path planning algorithm for unmanned surface vehicle formations in a practical maritime environment

2015

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a b s t r a c tUnmanned surface vehicles (USVs) have been deployed over the past decade. Current USV platforms are generally of small size with low payload capacity and short endurance times. To improve effectiveness there is a trend to deploy multiple USVs as a formation fleet. This paper presents a novel computer based algorithm that solves the problem of USV formation path planning. The algorithm is based upon the fast marching (FM) method and has been specifically designed for operation in dynamic environments using the novel constrained FM method. The constrained FM method is able to model the dynamic behaviour of moving ships with efficient computation time. The algorithm has been evaluated using a range of tests applied to a simulated area and has been proved to work effectively in a complex navigation environment.

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“…For a USV, a fantastic maneuverability can not only insure to execute tasks more flexible, but also make a contribution to improving its survivability. Considering the International Regulations for Preventing Collisions at Sea (COLREGS), many researchers concentrate on obstacle avoidance and path planning for USV [1][2][3]. Moreover, a motion planning method of USV under relative coordinates was described [4].…”

Section: Introductionmentioning

confidence: 99%

System Identification Methodology Preliminary Research on Maneuvering Motion of a New Type Unmanned Surface Vehicle

Ma¹,

Wang²,

Li³

et al. 2015

Advances in Intelligent Systems Research

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Abstract.A new type unmanned surface vehicle (USV) concept is proposed in this paper. Then, the intelligent autonomous vehicle model is designed and fabricated. Self-propelled model test of the new type USV is carried out, which is mainly turning test. According to the turning diameters in various speed and rudder angles under different control voltages and displacements as well as other researchers′ experience, the turning motion identification objective function of the new type USV is established. The new type USV maneuvering motion system identification program based on chaotic genetic optimum algorithm is compiled via VB.NET language. The equivalent mathematical model of turning motion is obtained after identified calculating. The reliability of this identification methodology is verified after comparing the identified results with test data, which can provide a kind of technical support in lucubrating the maneuvering motion pattern of the new type USV.

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Angular rate-constrained path planning algorithm for unmanned surface vehicles

Cited by 118 publications

References 18 publications

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

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

Path planning algorithm for unmanned surface vehicle formations in a practical maritime environment

System Identification Methodology Preliminary Research on Maneuvering Motion of a New Type Unmanned Surface Vehicle

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