Applying the self-tuning fuzzy control with the image detection technique on the obstacle-avoidance for autonomous underwater vehicles

This study develops a heuristic searching technique for obstacle-avoidance of autonomous underwater vehicles (AUVs) in varying ocean environments by using the self-tuning fuzzy controller. The corresponding hydrodynamic coefficients for the AUV are obtained by the test of Planar Motion Mechanism (PMM), which serves as the important data inputs for the control system. Subsequently, the self-tuning fuzzy controller would be adopted to command the propulsion of AUVs. The function of obstacle-avoidance is based on the underwater image detection method with the BK triangle sub-product of fuzzy relations which can evaluate the safety and remoteness of the candidate routes and the successive optimal strategic routing can then be selected. In the present simulations, the current effect is used to investigate the maneuvering performance of obstacle-avoidance. Eventually, the present study indicates that the self-tuning fuzzy controller, combined with the image detection technique based on BK triangle sub-product of fuzzy relations, is verified to be a useful searching technique for obstacle-avoidance of AUVs in depth variation.

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Development of a Multi-Platform Obstacle Avoidance System for Autonomous Underwater Vehicles

2020 IEEE/OES Autonomous Underwater Vehicles Symposium (AUV)(50043) ◽

10.1109/auv50043.2020.9267942 ◽

2020 ◽

Cited By ~ 1

Author(s):

Francesco Fanelli ◽

Davide Fenucci ◽

Rachel Marlow ◽

Miles Pebody ◽

Alexander B. Phillips

Keyword(s):

Obstacle Avoidance ◽

Autonomous Underwater Vehicles ◽

Underwater Vehicles

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Publish and Subscribe-Based Formation and Containment Control of Heterogeneous Robotic System with Actuator Time Delay

Applied Sciences ◽

10.3390/app11199145 ◽

2021 ◽

Vol 11 (19) ◽

pp. 9145

Author(s):

Siddig M. Elkhider ◽

Omar Al-Buraiki ◽

Sami El-Ferik

Keyword(s):

Time Delay ◽

Obstacle Avoidance ◽

Data Transmission ◽

Data Exchange ◽

Autonomous Underwater Vehicles ◽

Underwater Vehicles ◽

Containment Control ◽

L1 Adaptive Control ◽

Navigation Algorithm ◽

Local Controller

This paper addresses the problem of controlling a heterogeneous system composed of multiple Unmanned Aerial Vehicles (UAVs) and Autonomous Underwater Vehicles (AUVs) for formation and containment maintenance. The proposed approach considers actuator time delay and, in addition to formation and containment, considers obstacle avoidance, and offers a robust navigation algorithm and uses a reliable middleware for data transmission and exchange. The methodology followed uses both flocking technique and modified L1 adaptive control to ensure the proper navigation and coordination while avoiding obstacles. The data exchange between all the agents is provided through the data distribution services (DDS) middleware, which solves the interoperability issue when dealing with heterogeneous multiagent systems. The modified L1 controller is a local controller for stabilizing the dynamic model of each UAV and AUV, and the flocking approach is used to coordinate the followers around the leader or within the space delimited by their leaders. Potential Field (PF) allows obstacle avoidance during the agents’ movement. The performance of the proposed approach under the considerations mentioned above are verified and demonstrated using simulations.

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Simulation Study for Obstacle Avoidance of Autonomous Underwater Vehicles

Journal of Coastal Research ◽

10.2112/jcr-si108-021.1 ◽

2020 ◽

Vol 108 (sp1) ◽

Author(s):

Yixin Li ◽

Minge Zhang

Keyword(s):

Obstacle Avoidance ◽

Simulation Study ◽

Autonomous Underwater Vehicles ◽

Underwater Vehicles

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A Robust Adaptive Interval Type-2 Fuzzy Control for Autonomous Underwater Vehicles

2019 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology (IAICT) ◽

10.1109/iciaict.2019.8784855 ◽

2019 ◽

Cited By ~ 1

Author(s):

Ayad Al-Mahturi ◽

Fendy Santoso ◽

Matthew A. Garratt ◽

Sreenatha G. Anavatti

Keyword(s):

Fuzzy Control ◽

Autonomous Underwater Vehicles ◽

Underwater Vehicles ◽

Robust Adaptive ◽

Interval Type

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A Novel Classified Self-Organising Map Applied to Task Assignment

Journal of Navigation ◽

10.1017/s037346332000020x ◽

2020 ◽

Vol 73 (5) ◽

pp. 1129-1145

Author(s):

Yun Qu ◽

Daqi Zhu

Keyword(s):

Autonomous Underwater Vehicles ◽

Task Assignment ◽

Underwater Vehicles ◽

The Self ◽

Sensor Nodes ◽

Sensor Technology ◽

The Novel ◽

Total Energy Consumption ◽

Map Algorithm ◽

Self Organising Map

With the development of sensor technology, sensor nodes are increasingly being used in underwater environments. The strategy presented in this paper is designed to solve the problem of using a limited number of autonomous underwater vehicles (AUVs) to complete tasks such as data collection from sensor nodes when the number of AUVs is less than the number of target sensors. A novel classified self-organising map algorithm is proposed to solve the problem. First, according to the K-means algorithm, targets are classified into groups that are determined by the number of AUVs. Second, according to the self-organising map algorithm, AUVs are matched with groups. Third, each AUV is provided with the accessible order of the targets in the group. The novel classified self-organising map algorithm can be used not only to reduce the total energy consumption in a multi-AUV system, but also to give the most efficient accessible order of targets for AUVs. Results of simulations conducted to prove the applicability of the algorithm are given.

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