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Adaptive Re-planning of AUVs for Environmental Sampling Missions: A Fuzzy Decision Support System Based on Multi-objective Particle Swarm Optimization

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Abstract

This paper presents the first attempt at formulating the autonomous underwater vehicles (AUVs) adaptive sampling problem considering a spatiotemporal ocean environment and energy constraints. A path re-planning system that applies fuzzy logic in decision-making is then proposed to increase the sampling efficiency and reduce power consumption to complete the mission. The goal of adaptive sampling is to plan the trajectories of gliders or AUVs to collect ocean measurements located in areas most relevant to the phenomena under investigation. However, in an actual marine environment, common conditions exist under which AUVs need to adjust their routes as the phenomena change over time. These conditions also require the monitoring of AUV energy consumption under the effect of currents that vary in space and time. This two-objective optimization problem is solved using the multi-objective particle swarm optimization algorithm to produce a set of solutions. A new fuzzy comprehensive evaluation (FCE) method, based on decision-making using fuzzy logic, is then applied to select the optimum route from the solutions by adapting and regenerating trajectories according to the ocean forecast system, analysing different scenarios, and including constraints of the total operational time and energy restrictions. Several Monte Carlo simulations are conducted to evaluate the performance and robustness of strategies determined via FCE for various scenarios.

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Abbreviations

AUV:

Autonomous underwater vehicle

FCE:

Fuzzy comprehensive evaluation

MOPSO:

Multi-objective particle swarm optimization

ST:

Periodic sampling time horizon

TT:

Limited total operational time

G :

Grey value

E :

Energy consumption

EL:

Energy limit

PM:

Plausibility measure

BM:

Belief measure

VS:

Very sufficient

SS:

Somewhat sufficient

NM:

Normal

SP:

Somewhat poor

VP:

Very poor

RI:

Rapidly increase

SI:

Slowly increase

ST:

Steady

SD:

Slowly decrease

RD:

Rapidly decrease

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Acknowledgements

This research was supported by the Shanghai Sailing Program under Grant 17YF1409600 and Qingdao National Laboratory for Marine Science and Technology under Grant QNLM2016ORP0104.

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Authors and Affiliations

  1. State Key Laboratory of Ocean Engineering, Shanghai Jiao Tong University, Shanghai, China

    Hexiong Zhou, Zheng Zeng & Lian Lian

  2. College of Automation, Harbin Engineering University, Harbin, China

    Hexiong Zhou

  3. Institute of Oceanology, Shanghai Jiao Tong University, Shanghai, China

    Zheng Zeng & Lian Lian

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  1. Hexiong Zhou
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Correspondence to Zheng Zeng.

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Zhou, H., Zeng, Z. & Lian, L. Adaptive Re-planning of AUVs for Environmental Sampling Missions: A Fuzzy Decision Support System Based on Multi-objective Particle Swarm Optimization. Int. J. Fuzzy Syst. 20, 650–671 (2018). https://doi.org/10.1007/s40815-017-0398-7

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