Parameter Search for a Small Swarm of AUVs Using Particle Swarm Optimisation

Tholen, Christoph; Nolle, Lars

doi:10.1007/978-3-319-71078-5_32

Cited by 7 publications

(6 citation statements)

References 16 publications

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“…PSO models the behaviour of social entities for collaboration to locate a target [15]. Each particle moves towards the target by amending its velocity.…”

Section: Potential Cooperative Search Algorithmsmentioning

confidence: 99%

“…Only three AUVs can be used as search agents. For both PSO and all-to-one SOMA, the possibilities of failure is high with only three search agents [15]. A possible way to overcome this problem is to make AUVs focus on exploration more than exploitation.…”

Section: Exploration With a Small Swarmmentioning

confidence: 99%

“…For the PSO-based cooperative search algorithm, the cognitive scaling, the social scaling and the inertia weight parameters were set to 0.5, 1.5 and 1.5, respectively [15]. The worst performing AUV uses global information to generate a new solution with a probability of 0.1.…”

Section: Simulation Of Auvs As Search Agentsmentioning

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

Self Cite

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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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“…PSO models the behaviour of social entities for collaboration to locate a target [15]. Each particle moves towards the target by amending its velocity.…”

Section: Potential Cooperative Search Algorithmsmentioning

confidence: 99%

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

Self Cite

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“…The use of autonomous vehicles performing observation tasks has increased in recent years. Unmanned aerial vehicles (UAV), autonomous surface vehicles (ASV) and autonomous underwater vehicles (AUV) or similar vehicle types are typically equipped with multiple sensors in order to observe environmental phenomena and to search for features of interest [1]. Those vehicles are often limited in resources.…”

Section: Introductionmentioning

confidence: 99%

“…One example is the search for the source of a contamination [1]. The mission time for observing indicating phenomena depends on the available vehicle resources.…”

Section: Introductionmentioning

confidence: 99%

Window Operators for Processing Spatio-Temporal Data Streams on Unmanned Vehicles

Werner

Brinkhoff

2020

AGILE GIScience Ser.

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Abstract. Unmanned aerial and submersible vehicles are used in an increasing number of applications especially for data collection in misanthropic environments. During a mission, such vehicles generate multiple spatio-temporal data streams suitable to be processed by data stream management systems (DSMS). The main approach of a DSMS is limiting the elements of a stream by using sliding and tilting windows with time intervals as temporal condition. However, due to varying vehicle speed and limited on-board resources, such temporal windows do not provide adequate support for spatio-temporal problems. For solving this problem, we propose a set of six new spatio-temporal window operators in this paper. This set comprises of sliding distance, tilting distance, tilting waypoint, session distance, jumping distance and an area window to limit stream elements based on spatial conditions. Each of the listed operators provides an individual behaviour to support sophisticated applications like spatial interpolation and forecasting. An evaluation based on an example trajectory shows the benefit of the presented operators for spatio-temporal applications.

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Managing Spatio-Temporal Data Streams on AUVs

Werner

Brinkhoff

2018

2018 IEEE/OES Autonomous Underwater Vehicle Workshop (AUV)

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Parameter Search for a Small Swarm of AUVs Using Particle Swarm Optimisation

Cited by 7 publications

References 16 publications

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

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

Window Operators for Processing Spatio-Temporal Data Streams on Unmanned Vehicles

Managing Spatio-Temporal Data Streams on AUVs

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