Abstract
In response to the issue of close-range adversarial pursuit-evasion involving unmanned surface vessel (USV), this paper proposes a model predictive control-based algorithm for USV pursuit-evasion games. Firstly, considering the inherent variability among USV systems, the paper employs identification experiments by collecting data from the USV’ movements to obtain a more accurate kinematic model. Secondly, specific criteria are established to determine successful pursuit or evasion, taking into account the requirements of speed and safety. Considering the kinematic capabilities and attack angles of the USV, a Particle Swarm Optimization (PSO) algorithm is employed to dynamically select suitable velocities and headings for executing tactical maneuvers during pursuit and evasion scenarios. Moreover, to minimize errors between the USV subsystems and mitigate the impact of external factors, the identification model is integrated with the mechanism model to construct a comprehensive pursuit-evasion model, thereby improving the overall accuracy and reliability of the model. Finally, through simulation experiments, the proposed pursuit-evasion method is thoroughly validated in terms of its feasibility and effectiveness.
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This work was supported by program of shanghai academic research leader (Grant No. 20XD1421700).
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Peng, Y. et al. (2023). Model Predictive Control-Based Pursuit-Evasion Games for Unmanned Surface Vessel. In: Yang, H., et al. Intelligent Robotics and Applications. ICIRA 2023. Lecture Notes in Computer Science(), vol 14274. Springer, Singapore. https://doi.org/10.1007/978-981-99-6501-4_23
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DOI: https://doi.org/10.1007/978-981-99-6501-4_23
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