A multi-objective bi-level task planning strategy for UUV target visitation in ocean environment

Li, Tianbo; Sun, Siqing; Wang, Peng; Dong, Huachao; Wang, Xinjing

doi:10.1016/j.oceaneng.2023.116022

Cited by 5 publications

(1 citation statement)

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“…AUV cluster scheduling refers to the assignment of tasks, path planning, and coordinated control of a cluster composed of multiple AUVs. In order to improve the efficiency of the overall mission, the allocation of tasks for multiple AUVs is determined based on the cost and requirements of each task [1,2]. The problem studied in this paper consists of three parts: heterogeneous AUV cluster grouping, task allocation within AUV teams, and path planning.…”

Section: Auv Cluster Schedulingmentioning

confidence: 99%

Research on the Heterogeneous Autonomous Underwater Vehicle Cluster Scheduling Problem Based on Underwater Docking Chambers

Wang,

Tao,

et al. 2024

JMSE

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The onboard energy supply of Autonomous Underwater Vehicles (AUVs) is one of the main limiting factors for their development. The existing methods of deploying and retrieving AUVs from mother ships consume a significant amount of energy during submerging and surfacing, resulting in a small percentage of actual working time. Underwater docking chambers provide support to AUVs underwater, saving their precious energy and addressing this issue. When an AUV cluster is assigned multiple tasks, scheduling the cluster becomes essential, and task allocation and path planning are among the core problems in AUV cluster scheduling research. In this paper, based on the underwater docking chamber, an Improved Genetic Local Search Algorithm with Prior Knowledge (IGLSAPK) is proposed to simultaneously solve the task allocation and path planning problems. Under constraints such as onboard energy supply, AUV quantity, and AUV type, the algorithm groups AUVs, assigns tasks, and plans paths to accomplish tasks at different locations, aiming to achieve overall efficiency. The algorithm first generates an initial population using prior knowledge to improve its search efficiency. It then combines an improved local search algorithm to efficiently solve large-scale, complex, and highly coupled problems. The algorithm has been evaluated through simulation experiments and comparative experiments, and the results demonstrate that the proposed algorithm outperforms other algorithms in terms of speed and optimality. The algorithm presented in this paper addresses the grouping, task allocation, and path planning problems in heterogeneous AUV clusters. Its practical significance lies in its ability to handle tasks executed by a heterogeneous AUV group, making it more practical compared to previous algorithms.

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Section: Auv Cluster Schedulingmentioning

confidence: 99%