Towards Reducing the Impact of Localisation Errors on the Behaviour of a Swarm of Autonomous Underwater Vehicles

Localisation errors have a great impact on Autonomous Underwater Vehicles (AUVs) as search agents. Different approaches for solving the localisation problem can be used and combined together for greater accuracy in estimating AUVs’ locations. The effect of localisation errors on locating a target can be lightened by designing a search algorithm that avoids extensive use of exact lo-cation information. In this paper, two cooperative search algorithms are proposed and evaluated. In these algorithms, a high-level mechanism is employed for building a global view of the search space using minimum possible search information. These algorithms rely on low-level search algorithms with exploring roles. Particle Swarm Optimisation (PSO) and all-to-one Self-Organising Migrating Algorithm (SOMA) are selected as high-level mechanisms. The conducted experiments demonstrate that both algorithms show a robust behaviour within a range of localisation errors.