4.7 Article

A Hierarchical Approach to Intelligent Mission Planning for Heterogeneous Fleets of Autonomous Underwater Vehicles

Journal

JOURNAL OF MARINE SCIENCE AND ENGINEERING
Volume 10, Issue 11, Pages -

Publisher

MDPI
DOI: 10.3390/jmse10111639

Keywords

multi-robot systems; autonomous underwater vehicles; mission planning; group coordination; shift scheduling; hybrid evolutionary algorithm

Funding

  1. Russian Science Foundation [22-29-00819]
  2. Russian Foundation for Basic Research [20-07-00397]

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The rapid development of marine robotic technology has significantly improved the self-sufficiency of AUVs. Ensuring efficient interaction of multiple autonomous vehicles in complex dynamic missions requires intelligent adaptive methods under severe operating constraints. The study presents a novel hierarchical approach for the group control system of large heterogeneous fleets of AUVs, demonstrating high efficiency through computational experiments.
The rapid development of marine robotic technology in recent decades has resulted in significant improvements in the self-sufficiency of autonomous underwater vehicles (AUVs). However, simple scenario-based approaches are no longer sufficient when it comes to ensuring the efficient interaction of multiple autonomous vehicles in complex dynamic missions. The necessity to respond cooperatively to constant changes under severe operating constraints, such as energy or communication limitations, results in the challenge of developing intelligent adaptive approaches for planning and organizing group activities. The current study presents a novel hierarchical approach to the group control system designed for large heterogeneous fleets of AUVs. The high-level core of the approach is rendezvous-based mission planning and is aimed to effectively decompose the mission, ensure regular communication, and schedule AUVs recharging activities. The high-level planning problem is formulated as an original acyclic variation of the inverse shift scheduling problem, which is NP-hard. Since regular schedule adjustments are supposed to be made by the robots themselves right in the course of the mission, a meta-heuristic hybrid evolutionary algorithm is developed to construct feasible sub-optimal solutions in a short time. The high efficiency of the proposed approach is shown through a series of computational experiments.

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