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 S a t u r a t i o n , 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.
Highlights
In line with the growing interest in the ocean for civilian and military applications, there has been an increasing demand for the autonomy of unmanned surface vehicles (USVs) with advanced automatic navigation systems (ANSs) [1]
To ensure that a fully-autonomous USV travels in a realistic sea environment, the ANS is composed of a path planning subsystem (PPS) and a collision avoidance subsystem (CAS)
A complete ANS is presented with PPS (DTFMS) and CAS (FCS-Model predictive control (MPC)) in a realistic sea environment
Summary
In line with the growing interest in the ocean for civilian and military applications, there has been an increasing demand for the autonomy of unmanned surface vehicles (USVs) with advanced automatic navigation systems (ANSs) [1]. 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 These algorithms have good real-time performance, they are difficult to use in complex and irregular environments. A fast and safe CAS is proposed based on FCS-MPC, and it has the ability for trajectory tracking and local collision avoidance for underactuating USVs. to fully evaluate the safety of the predicted trajectories, its security evaluation comes from the potential map of the DTFMS method.
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