Local reactive obstacle avoidance approach for high-speed unmanned surface vehicle

Tang, Pingpeng; Zhang, Rubo; Liu, De Li; Huang, Lihua; Liu, Guanqun; Deng, Tingquan

doi:10.1016/j.oceaneng.2015.06.055

Cited by 61 publications

(61 citation statements)

References 29 publications

(36 reference statements)

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“…A potential field algorithm was proposed to realize the automatic real-time obstacle avoidance of sailing. Tang et al [5] divided the local static obstacle avoidance algorithms into two types, namely, path-searchingbased local path planning and behaviour-based reactive obstacle avoidance method. Yang et al [6] proposed an improved A*-based algorithm, which is called direction priority sequential selection, to solve the problem of local static obstacle avoidance.…”

Section: State Of the Artmentioning

confidence: 99%

Simulation on Local Obstacle Avoidance Algorithm for Unmanned Surface Vehicle

Wang¹,

Mao²,

Du³

2016

Int. j. simul. model.

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Unmanned surface vehicle (USV) is an important autonomous marine vehicle. The safe navigation of USV is directly determined by the local obstacle avoidance because it must avoid real-time local obstacle in the global path planning in a three-dimensional environment. Therefore, efficient algorithms of real-time local obstacle avoidance for USV are a critical issue. In this study, a new threelayered architecture for local obstacle avoidance algorithm was proposed to solve the local obstacle avoidance problem. First, real environment and obstacle models were established in the polar coordinate. The known static path-planning method was conducted based on particle swarm optimization (PSO). Second, the method was integrated with marine rules based on PSO. Third, an obstacle avoidance method under unknown environment was created based on rolling windows. Finally, a simulation experimental platform was developed to verify the feasibility and effectiveness of the aforementioned measure. Result shows that the proposed algorithm can effectively avoid local obstacles of USV at a computation time of less than 2 s. The USV avoids the obstacles smoothly and reaches the desired destination with complex requirements. The simulation results also demonstrate the promising application of the proposed method in studying the path planning of USV. This method can address the issues of real-time local obstacle avoidance of USV.

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Section: State Of the Artmentioning

confidence: 99%

Simulation on Local Obstacle Avoidance Algorithm for Unmanned Surface Vehicle

Wang¹,

Mao²,

Du³

2016

Int. j. simul. model.

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show abstract

“…A reliable autonomous navigation system with the capabilities of path-planning, obstacle avoidance, and auto-guidance is essential for an USV to deal with various and dynamic marine situations [3]. The most widely used obstacle avoidance and path-planning algorithms include artificial potential fields (APF) [4], rapidly exploring random trees (RRTs) [5], greedy mechanism-based particle swarm optimization (PSO) [6], and grid map-based path-planning algorithms (e.g., A* algorithm [7], Field D* [8], and Theta* [9]).…”

Section: Introductionmentioning

confidence: 99%

Iterative Learning-Based Path and Speed Profile Optimization for an Unmanned Surface Vehicle

Yang

Jun-nan

et al. 2020

Sensors

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Most path-planning algorithms can generate a reasonable path by considering the kinematic characteristics of the vehicles and the obstacles in hydrographic survey activities. However, few studies consider the influence of vehicle dynamics, although excluding system dynamics may considerably damage the measurement accuracy especially when turning at high speed. In this study, an adaptive iterative learning algorithm is proposed to optimize the turning parameters, which accounts for the dynamic characteristics of unmanned surface vehicles (USVs). The resulting optimal turning radius and speed are used to generate the path and speed profiles. The simulation results show that the proposed path-smoothing and speed profile design algorithms can largely increase the path-following performance, which potentially can help to improve the measurement accuracy of various activities.

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“…Generally, the collision avoidance system (CAS) requires the vessel to finish the task safely, stably and rapidly, i.e., attainability, safety, stability and rapidity [2]. For high speed USV, [10] proposed a local reaction avoidance method. However, we find that the realization of CAS is divided into path planning and the control system.…”

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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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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Local reactive obstacle avoidance approach for high-speed unmanned surface vehicle

Cited by 61 publications

References 29 publications

Simulation on Local Obstacle Avoidance Algorithm for Unmanned Surface Vehicle

Simulation on Local Obstacle Avoidance Algorithm for Unmanned Surface Vehicle

Iterative Learning-Based Path and Speed Profile Optimization for an Unmanned Surface Vehicle

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

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