A Multi-Task Algorithm for Autonomous Underwater Vehicles 3D path planning

When considering multiple Autonomous Underwater Vehicles (AUV) path planning simultaneously, problems can be seen as multi-task optimization (MTO) problems. The Multifactorial evolutionary algorithm (MFEA) has many obvious advantages in handling multi-task optimization, but it is rarely used in thre...

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Bibliographic Details
Published in2020 3rd International Conference on Unmanned Systems (ICUS) pp. 972 - 977
Main Authors Hu, Hao, Zhou, Yongjian, Wang, Tonghao, Peng, Xingguang
Format Conference Proceeding
LanguageEnglish
Published IEEE 27.11.2020
Subjects
3D model
Genetic algorithms
MFEA
multi-task optimization
Oceans
Optimization
Path planning
Solid modeling
Task analysis
Three-dimensional displays
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DOI10.1109/ICUS50048.2020.9274809

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Summary:When considering multiple Autonomous Underwater Vehicles (AUV) path planning simultaneously, problems can be seen as multi-task optimization (MTO) problems. The Multifactorial evolutionary algorithm (MFEA) has many obvious advantages in handling multi-task optimization, but it is rarely used in three-dimensional (3D) path planning. Therefore, using the individual gradient based on MFEA (MFEA-IG), this paper solves the global path planning problem of AUV in the 3D environment. We construct a 3D space model, effectively reducing the computational complexity from 3D to two-dimensional (2D). Besides, we also consider the impact of ocean currents on path planning. In this paper, both MFEA-IG and MFEA are used for path planning, and time cost is selected as their optimization target. Experimental results show that the two algorithms can be effectively used for AUV 3D path planning, and MFEA-IG has a better result than MFEA.
DOI:10.1109/ICUS50048.2020.9274809