Collision Avoidance in Multiple-Ship Situations by Distributed Local Search

Abstract

As vital transportation carriers in trade, ships have the advantage of stability, economy, and bulk capacity over airplanes, trucks, and trains. Even so, their loss and cost due to collisions and other accidents exceed those of any other mode of transportation. To prevent ship collisions many ways have been suggested, e.g., the 1972 COLREGs which is the regulation for preventing collision between ships. Technologically speaking, many related studies have been conducted. The term “Ship domain” involves that area surrounding a ship that the navigator wants to keep other ships clear of. Ship domain alone is not sufficient, however, for enabling one or more ships to simultaneously determine the collision risk for all of the ships concerned. Fuzzy theory is useful in helping ships avoid collision in that fuzzy theory may define whether collision risk is based on distance to closest point of approach, time to closest point of approach, or relative bearing – algorithms that are difficult to apply to more than one ships at one time. The main purpose of this study is thus to reduce collision risk among multiple ships using a distributed local search algorithm (DLSA). By exchanging information on, for example, next-intended courses within a certain area among ships, ships having the maximum reduction in collision risk change courses simultaneously until all ships approach a destination without collision. In this paper, we introduce distributed local search and explain how it works using examples. We conducted experiments to test distributed local search performance for certain instances of ship collision avoidance. Experiments results showed that in most cases, our proposal applies well in ship collision avoidance amongmultiple ships.

Distributed Stochastic Search Algorithm for Multi-ship Encounter Situations

“…70 such complex relations among multiple ships, many-to-many situations should be handled directly by modelling ships as agents who can communicate their next-intended courses, namely intentions, with each other to find their safest courses autonomously. For many-to-many situations, few methods have been suggested in the literature except for our distributed algorithms (Kim et al, 2014;, which are in a form of peer-to-peer communication protocols. With these algorithms, the ships can find the safest courses by themselves without any instruction from a centralised system, such as a Vessel Traffic Service (VTS) centre.…”

“…With these algorithms, the ships can find the safest courses by themselves without any instruction from a centralised system, such as a Vessel Traffic Service (VTS) centre. In the Distributed Local Search Algorithm (DLSA) (Kim et al, 2014), each ship searches for a safer course within her own local view by exchanging intentions with target ships. The Distributed Tabu Search Algorithm (DTSA) Kim et al (2015) enhances DLSA with the tabu search technique to escape from a Quasi-Local Minimum (QLM) in which DLSA sometimes becomes trapped.…”

“…Its framework is given in Figure 1. (Fujii and Tanaka, 1971), (Goodwin, 1975), (Szlapczynski, 2006), (Wang et al, 2009) ship domain one-to-many centralised (Tsou and Hsueh, 2010), (Lazarowska, 2015) ant colony one-to-many centralised genetic algorithm one-to-many centralised (Lee et al, 2004) fuzzy theory one-to-many centralised (Lamb and Hunt, 1995;2000) probability one-to-many centralised (Szlapczynski, 2011; evolutionary algorithm many-to-many centralised (Hornauer, 2013), (Hornauer et al, 2015) Bayesian model many-to-many decentralised (Hu et al, 2008) negotiation one-to-one decentralised (Kim et al, 2014) local search many-to-many distributed (Kim et al, 2015) tabu search many-to-many distributed By the control procedure, a ship decides whether to proceed to the next position. If a ship does not have any target ship in the area within a certain distance from her current position and also has not yet arrived at her destination, she moves to the next position.…”

“…Distributed Local Search Algorithm. DLSA is a simple distributed algorithm to minimise the total sum of costs over the ships by letting them exchange their current intentions and improvements with each other (Kim et al, 2014). It is an incomplete optimisation algorithm, which is not guaranteed to find an optimal solution.…”

A Simple Approach to Solving Cooperative Path-Finding as Propositional Satisfiability Works Well

“…There are many practical motivations for CPF ranging from unit navigation in computer games [24] to item relocation in automated storage (see KIVA robots [13]). Interesting motivations can be also found in traffic where problems like vessel avoidance at sea are of great practical importance [12]. An analogical challenge appears in the air where availability of drones implies need for developing cooperative air traffic control mechanisms [15].…”