Trajectory planning is a crucial task in designing the navigation systems of automatic underwater vehicles (AUVs). Due to the complexity of underwater environments, decision makers may hope to obtain multiple alternative trajectories in order to select the best. This paper focuses on the multiple-trajectory planning (MTP) problem, which is a new topic in this field. First, we establish a comprehensive MTP model for AUVs, by taking into account the complex underwater environments, the efficiency of each trajectory, and the diversity among different trajectories, simultaneously. Then, to solve the MTP, we develop an ant colony-based trajectory optimizer, which is characterized by a niching strategy, a decayed alarm pheromone measure, and a diversified heuristic measure. The niching strategy assists in identifying and maintaining a diverse set of high-quality solutions. The use of decayed alarm pheromone and diversified heuristic further improves the search effectiveness and efficiency of the algorithm. Experimental results on practical datasets show that our proposed algorithm not only provides multiple AUV trajectories for a flexible choice, but it also outperforms the state-of-the-art algorithms in terms of the single trajectory efficiency.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    MTrajPlanner: A Multiple-Trajectory Planning Algorithm for Autonomous Underwater Vehicles


    Contributors:
    Gong, Yue-Jiao (author) / Huang, Ting (author) / Ma, Yi-Ning (author) / Jeon, Sang-Woon (author) / Zhang, Jun (author)


    Publication date :

    2023-04-01


    Size :

    6129463 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English



    Vertical Trajectory Planning for Autonomous Vehicles

    Jurisch, Matthias / Koch, Thorsten | TIBKAT | 2021


    Trajectory Planning for Autonomous Vehicles at Autonomous Intersection

    Chen, Jing / Mu, Chen / Zhao, Lu | TIBKAT | 2020


    Vertical Trajectory Planning for Autonomous Vehicles

    Jurisch, Matthias / Koch, Thorsten | Springer Verlag | 2021



    Liveness-Based RRT Algorithm for Autonomous Underwater Vehicles Motion Planning

    Yang Li / Fubin Zhang / Demin Xu et al. | DOAJ | 2017

    Free access