Nonlinear Model Predictive Control for Enhanced Navigation of Autonomous Surface Vessels
<|reference_start|>Nonlinear Model Predictive Control for Enhanced Navigation of Autonomous Surface Vessels: This article proposes an approach for collision avoidance, path following, and anti-grounding of autonomous surface vessels under consideration of environmental forces based on Nonlinear Model Predictive Control (NMPC). Artificial Potential Fields (APFs) set the foundation for the cost function of the optimal control problem in terms of collision avoidance and anti-grounding. Depending on the risk of a collision given by the resulting force of the APFs, the controller optimizes regarding an adapted heading and travel speed by additionally following a desired path. For this purpose, nonlinear vessel dynamics are used for the NMPC. To extend the situational awareness concerning environmental disturbances impacted by wind, waves, and sea currents, a nonlinear disturbance observer is coupled to the entire NMPC scheme, allowing for the correction of an incorrect vessel motion due to external forces. In addition, the most essential rules according to the Convention on the International Regulations for Preventing Collisions at Sea (COLREGs) are considered. The results of the simulations show that the proposed framework can control an autonomous surface vessel under various challenging scenarios, including environmental disturbances, to avoid collisions and follow desired paths.<|reference_end|>
@article{menges2024nonlinear,
    title={Nonlinear Model Predictive Control for Enhanced Navigation of Autonomous
  Surface Vessels},
    author={Daniel Menges, Trym Tengesdal, Adil Rasheed},
    journal={arXiv preprint arXiv:2403.19028},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.19028},
    primaryClass={eess.SY cs.SY}
}