A constrained A* approach towards optimal path planning for an unmanned surface vehicle in a maritime environment containing dynamic obstacles and ocean currents

Abstract

Efficient path planning is a critical issue for the navigation of modern unmanned surface vehicles (USVs) characterized by a complex operating environment having dynamic obstacles with a spatially variable ocean current. The current work explores an A* approach with an USV enclosed by a circular boundary as a safety distance constraint on generation of optimal waypoints to resolve the problem of motion planning for an USV moving in a maritime environment. Unlike existing work on USV navigation using graph based methods, this study extends the implementation of the proposed A* approach in an environment cluttered with static and moving obstacles and different current intensities. The study also examines the effect of headwind and tailwind currents moving in clockwise and anti clockwise direction respectively of different intensities on optimal waypoints in a partially dynamic environment. The performance of the proposed approach is verified in simulations for different environmental conditions. The effectiveness of the proposed approach is measured using two parameters, namely, path length and computational time as considered in other research works. The results show that the proposed approach is effective for global path planning of USVs.

Shore Construction Detection by Automotive Radar for the Needs of Autonomous Surface Vehicle Navigation

“…In Reference [14,15], a numerically derived ship domain calculated on the basis of movement vectors is proposed. In Reference [16], a moving obstacle is modeled as an elliptical domain, according to the recommendations of the International Maritime Organization (IMO), and a complex system based on path planning with a constrained A* approach in a confined maritime environment is presented. The system takes account of both dynamic obstacles and ocean environmental effects.…”

A Method of Path Planning on Safe Depth for Unmanned Surface Vehicles Based on Hydrodynamic Analysis

“…Establishing a real ocean environment model is essential so that unmanned surface vehicles can perceive the external environment before path planning. Binarization processing is applied to build environment model via binarize remote sensing satellite is the majority of path planning [7,17,19,26,30]. Nevertheless, the depth of water is not considered, thus navigation along the planned path may be stranding.…”

“…The minimum cost function is used to search the minimum cost path from the starting node to the end node. The grid-based A* path search methods are mainly based on the center point of the grid [11][12][13][14][15][16] and grid vertexes [7,10,[17][18][19]. Grid method can be divided into a uniform grid and non-uniform grid with diverse shapes and sizes of the grid.…”

Modified A* Algorithm for Obstacle Avoidance for Unmanned Surface Vehicle

“…The more general algorithm, A*, which is the best-first search algorithm, is also used for path planning. An optimal path planning method has been shown to generate a feasible path using a constrained A* algorithm for a USV in a confined maritime environment, where dynamic obstacles are a concern (Singh et al, 2019). Several other methods have been used for path planning for marine vessels, including artificial potential field (Xie et al, 2014), fast marching (FM) (Liu and Bucknall, 2015), real-time R* (RTR*), and partitioned learning real-time A* (PLRTA*) (Cannon et al, 2012).…”