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A nature inspired collision avoidance algorithm for ships

Lazarowska A.

TransNav : International Journal on Marine Navigation and Safety of Sea Transportation

2023

Vol. 17 No. 2

341--346

Nature inspired algorithms are regarded as a powerful tool for solving real life problems. They do not guarantee to find the globally optimal solution, but can find a suboptimal, robust solution with an acceptable computational cost. The paper introduces an approach to the development of collision avoidance algorithms for ships based on the firefly algorithm, classified to the swarm intelligence methods. Such algorithms are inspired by the swarming behaviour of animals, such as e.g. birds, fish, ants, bees, fireflies. The description of the developed algorithm is followed by the presentation of simulation results, which show, that it might be regarded as an efficient method of solving the collision avoidance problem. Such algorithm is intended for use in the Decision Support System or in the Collision Avoidance Module of the Autonomous Navigation System for Maritime Autonomous Surface Ships.

DSSA+: distributed collision avoidance algorithm in an environment where both course and speed changes are allowed

Hirayama K., Miyake K., Shiotani T., Okimoto T.

TransNav : International Journal on Marine Navigation and Safety of Sea Transportation

2019

Vol. 13, no. 1

117--123

Distributed Stochastic Search Algorithm (DSSA) is one of state-of-the-art distributed algorithms for the ship collision avoidance problem. In DSSA, whenever a ship encounters with any number of other ships (neighboring ships), she will select her course with a minimum cost after coordinating their decisions with her neighboring ships. The original DSSA assumes that ships can change only their courses while keeping their speed considering kinematic properties of ships in general. However, considering future possibilities to address more complex situations that may cause ship collision or to deal with collision of other vehicles (such as mobile robots or drones), the options of speed changes are necessary for DSSA to make itself more flexible and extensive. In this paper, we present DSSA+, as a generalization of DSSA, in which speed change are naturally incorporated as decision variables in the original DSSA. Experimental evaluations are provided to show how powerful this generalization is.