Informed Search Patterns For Alleviating The Impact Of The Localisation Problem

El-Mihoub, Tarek A.; Tholen, Christoph; Nolle, Lars

doi:10.7148/2019-0037

Cited by 2 publications

(8 citation statements)

References 7 publications

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“…A search strategy that facilitates collaboration between a swarm of AUVs is needed to maximise the possibilities of locating a target [10], [7]. A cooperative search strategy should take into consideration the characteristics of the environment, the agents and the target [3]. Particle Swarm Optimisation (PSO) has been used as a collaborative search strategy [16].…”

Section: Potential Cooperative Search Algorithmsmentioning

confidence: 99%

“…However, it might face a premature convergence problem and can be distracted by localisation and the communication errors [16]. A single AUV in the presence of localisation errors can successfully locate a search target using search strategies that are based on coverage path planning [3]. These algorithms are also robust against premature convergence in a multiple SGDs environment.…”

Section: Potential Cooperative Search Algorithmsmentioning

confidence: 99%

“…This paper investigates the possibilities of extending the concept of alleviating the localisation problem used in [3] to cooperative search strategies. Based on this concept, the best potential cooperative strategies are those, which produce new solutions using the smallest possible set of solutions.…”

Section: Potential Cooperative Search Algorithmsmentioning

confidence: 99%

“…This also means a minimum need for communication between search agents and both algorithms are suitable for AUVs where communication problems are expected. Developing a reliable cooperative search of a swarm of AUVs necessitates considering constrains imposed by using AUVs as search agents [3]. The limitation in the number of AUVs, the energy and the limitation on movement can lead to modifying the potential search strategy.…”

Section: Outlines Of a Cooperative Search Algorithmmentioning

confidence: 99%

“…Agents can adopt search strategies, which are based on coverage path planning [3], as low-level search algorithms. Preplanned search paths can be assigned to agents to cover the search space.…”

Section: Exploration With a Small Swarmmentioning

confidence: 99%

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

El-Mihoub

Tholen

Nolle

2020

mendel

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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.

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Section: Potential Cooperative Search Algorithmsmentioning

confidence: 99%

Section: Potential Cooperative Search Algorithmsmentioning

confidence: 99%

Section: Potential Cooperative Search Algorithmsmentioning

confidence: 99%

Section: Outlines Of a Cooperative Search Algorithmmentioning

confidence: 99%

Section: Exploration With a Small Swarmmentioning

confidence: 99%

See 3 more Smart Citations