Automatic collision avoidance of ships

Abstract

One of the key elements in automatic simulation of ship manoeuvring in confined waterways is route finding and collision avoidance. This paper presents a new practical method of automatic trajectory planning and collision avoidance based on an artificial potential field and speed vector. Collision prevention regulations and international navigational rules have been incorporated into the algorithm. The algorithm is fairly straightforward and simple to implement, but has been shown to be effective in finding safe paths for all ships concerned in complex situations. The method has been applied to some typical test cases and the results are very encouraging

A review on improving the autonomy of unmanned surface vehicles through intelligent collision avoidance manoeuvres

“…The potential field method is adopted for static and dynamic obstacles with simulations for single and multiple vessel encounters exhibiting correct crossing and head on avoidance behaviour (Xue et al, 2009). Another reactive technique which has been investigated for this purpose is based on the Distributed Architecture for Mobile Navigation (DAMN), selecting an appropriate arc to follow based on individual weighting according to its intersection with obstacles.…”

Evolutionary Planning of Safe Ship Tracks in Restricted Visibility

“…There are also a number of EC-related approaches, such as: trajectory optimisation using a genetic annealing algorithm (Cheng and Liu, 2007) and ship collision avoidance route planning by ant colony algorithm (Tsou and Hsueh, 2010a). Apart from these, automatic collision avoidance of ships using artificial potential field and speed vector has also been used by Xue et al (2009). Summaries of applying EC methods to maritime collision avoidance and track planning have been presented in Yang et al (2006) and Statheros et al (2008) among others.…”

Evolutionary sets of safe ship trajectories with speed reduction manoeuvres within traffic separation schemes

“…This method combined evolutionary approach [9] with goals typical for methods based on games theory [7]. However, unlike other methods utilizing evolutionary algorithms [9,16], genetic algorithms [2,6,13] and related heuristics [8,12,14,15], instead of finding an optimal single ship trajectory only, the author's method searches for an optimal set of safe trajectories of all ships involved in an encounter. In the present paper the extensions of this method are described which allow to combine evolutionary trajectory planning with speed reduction manoeuvres.…”