Maintenance and transformation method for a multi-AUV dense formation

Author:

Chen Kuo123ORCID,Zhang Jin4,Yu Jiancheng12

Affiliation:

1. State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, China

2. Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang, China

3. University of Chinese Academy of Sciences, Beijing, China

4. College of Light Industry, Liaoning University, Shenyang, China

Abstract

Accurate formation maintenance and safe formation transformations are significant challenges for multiple autonomous underwater vehicle (multi-AUV) dense formations. To address these problems, an innovative control method for a multi-AUV dense formation is proposed. First, a model predictive controller (MPC) that considers AUV input constraints and external disturbances is designed such that a multi-AUV dense formation can accurately maintain a desired formation while tracking a reference trajectory. After that, at the kinematics level, an optimal path for a safe and efficient multi-AUV dense formation transformation is generated based on the Hungarian method. Furthermore, considering an underactuated and nonlinear AUV dynamics model at the dynamics level, a potential function based on collision avoidance is established. It is added to the MPC objective function to further guarantee the potential of the formation transformation. Finally, a multi-AUV dense formation maintenance simulation shows that the proposed method can guarantee higher trajectory tracking accuracy than other algorithms. A multi-AUV dense formation transformation simulation shows that the proposed method avoids the occurrence of cross paths and a safe distance between AUVs is always maintained. The above results demonstrate that multi-AUV dense formations can achieve accurate maintenance and safe transformations, and the proposed method is feasible and effective.

Funder

National Natural Science Foundation of China

Liaoning Revitalization Talents Program

Natural Science Foundation of Liaoning Province

State Key Laboratory of Robotics at Shenyang Institute of Automation, China

Publisher

SAGE Publications

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