Biogeography-based combinatorial strategy for efficient autonomous underwater vehicle motion planning and task-time management

Somaiyeh MahmoudZadeh, David Powers, Karl Sammut, Amirmehdi Yazdani

    Research output: Contribution to journalArticlepeer-review

    10 Citations (Scopus)


    Autonomous Underwater Vehicles (AUVs) are capable of spending long periods of time for carrying out various underwater missions and marine tasks. In this paper, a novel conflict-free motion planning framework is introduced to enhance underwater vehicle’s mission performance by completing maximum number of highest priority tasks in a limited time through a large scale waypoint cluttered operating field, and ensuring safe deployment during the mission. The proposed combinatorial route-path planner model takes the advantages of the Biogeography-Based Optimization (BBO) algorithm toward satisfying objectives of both higher-lower level motion planners and guarantees maximization of the mission productivity for a single vehicle operation. The performance of the model is investigated under different scenarios including the particular cost constraints in time-varying operating fields. To show the reliability of the proposed model, performance of each motion planner assessed separately and then statistical analysis is undertaken to evaluate the total performance of the entire model. The simulation results indicate the stability of the contributed model and its feasible application for real experiments.

    Original languageEnglish
    Pages (from-to)463-477
    Number of pages15
    JournalJournal of Marine Science and Application
    Issue number4
    Publication statusPublished - 1 Dec 2016


    • autonomous underwater vehicles
    • biogeography-based optimization
    • computational intelligence
    • evolutionary algorithms
    • route planning
    • underwater missions


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