Distributed Stochastic Search Algorithm for Multi-ship Encounter Situations

Abstract

Ship collision avoidance involves helping ships find routes that will best enable them to avoid a collision. When more than two ships encounter each other, the procedure becomes more complex since a slight change in course by one ship might affect the future decisions of the other ships. Two distributed algorithms have been developed in response to this problem: Distributed Local Search Algorithm (DLSA) and Distributed Tabu Search Algorithm (DTSA). Their common drawback is that it takes a relatively large number of messages for the ships to coordinate their actions. This could be fatal, especially in cases of emergency, where quick decisions should be made. In this paper, we introduce Distributed Stochastic Search Algorithm (DSSA), which allows each ship to change her intention in a stochastic manner immediately after receiving all of the intentions from the target ships. We also suggest a new cost function that considers both safety and efficiency in these distributed algorithms. We empirically show that DSSA requires many fewer messages for the benchmarks with four and 12 ships, and works properly for real data from the Automatic Identification System (AIS) in the Strait of Dover. K E Y W O R D S1. Ship collision avoidance.2. Distributed stochastic search algorithm. 3. Multiple ships control.

Distributed Multi-Objective Algorithm for Preventing Multi-Ship Collisions at Sea

“…The above four variables are transmitted through the communication between ships, and each ship has a specific and known collision avoidance strategy; thus, the behaviour of other ships can be considered when planning the route. Because it takes time to exchange information and plan the trajectory of each ship and the ships communicate with each other to determine the collision avoidance strategy, we set 3 min as the time step (Kim et al, 2017). Therefore, when a ship identifies a potential risk, the collision avoidance strategy is updated every 3 min.…”

“…The distributed tabu search algorithm (Kim et al, 2015) introduced a tabu search technique to avoid local optima. The distributed stochastic search algorithm (Kim et al, 2017) changes ship intentions in a random manner to reduce the number of communications between ships. In this paper, a distributed algorithm principle is applied, and information for the entire route is used in communication between ships.…”

A Comprehensive Review on Big Data-Based Potential Applications in Marine Shipping Management

“…In existing research on AIS data applications, the only field adopting, OR methods were route planning. Different efficient algorithms such as the genetic algorithm, other heuristic algorithms, and ant colony optimization were presented for route planning by Kim et al [45].…”

Monitoring of the process of safe divergence on the part of an unmanned vessel

“…A number of works are devoted to solving the main problems of automatic divergence, namely, the problem of assessing the situation and developing a solution for safe divergence [1][2][3][4].…”