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

Abstract

Effective 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.

Practical Moving Target Detection in Maritime Environments Using Fuzzy Multi-sensor Data Fusion

“…The speed or course changing is often slow to maintain the vessel steady. Therefore, a constant velocity (CV) model can be used to describe the state of the target [28]. The state vector is defined to include essential navigational data to assess the collision risk between the target and USV:…”

Intelligent Tracking of Moving Ships in Constrained Maritime Environments Using AIS

“…Based on the work in Liu et al (2017), an improved Kalman Filter based trajectory tracking algorithm (KFTTA) has been proposed to increase the accuracy of AIS information regardless of whether transmissions are regular or irregular. There are two processes composing the KFTTA namely the Updating Process and the Predicting Process (Figure 2).…”

The angle guidance path planning algorithms for unmanned surface vehicle formations by using the fast marching method

“…No more path smoother is required to process the path, and the path can be easily executed by a proper controller in practical applications (Álvarez et al, 2015). In the meantime, the FMM is fast in computational speed, which further promotes its utilisation in real-time navigation such as on autonomous underwater vehicle (Petres et al, 2007)), unmanned aerial vehicle (Garrido et al, 2013) and even the USV platforms (Liu et al, 2015a;Xu et al, 2013) However, the implementation of the FMM to USVs needs more considerations. A crucial factor is that USV, as a nonholonomic system, is underactuated during most of operation time making it have low manoeuvrability and motion flexibility.…”