Abstract
The development of intelligent task allocation and path planning algorithms for unmanned surface vehicles (USVs) is gaining significant interest, particularly in supporting complex ocean operations. This paper proposes an intelligent hybrid algorithm that combines task allocation and path planning to improve mission efficiency. The algorithm introduces a novel approach based on a self-attention mechanism (SAM) for intelligent task allocation. The key contribution lies in the integration of an adaptive distance field, created using the locking sweeping method (LSM), into the SAM. This integration enables the algorithm to determine the minimum practical sailing distance in obstacle-filled environments. The algorithm efficiently generates task execution sequences in cluttered maritime environments with numerous obstacles. By incorporating a safety parameter, the enhanced SAM algorithm adapts the dimensional influence of obstacles and generates paths that ensure the safety of the USV. The algorithms have been thoroughly evaluated and validated through extensive computer-based simulations, demonstrating their effectiveness in both simulated and practical maritime environments. The results of the simulations verify the algorithm’s capability to optimize task allocation and path planning, leading to improved performance in complex and obstacle-laden scenarios.
Published Version (
Free)
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
