Unmanned ship and unmanned aerial vehicle system cooperative control method based on hydrodynamic parameters and deep reinforcement learning, electronic equipment and readable storage medium

By proposing a cooperative control method for unmanned surface vessels (USVs) and unmanned aerial vehicles (UAVs) based on hydrodynamic parameters and deep reinforcement learning, the cooperative control problem of USVs and UAVs in complex sea conditions was solved, achieving efficient and adaptive cooperative operation and obstacle avoidance capabilities, and improving the stability and operational efficiency of the system.

CN120872027APending Publication Date: 2025-10-31JINING UNIV +1
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Patent Information

Application Number
CN202511180408.6
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-08-22
Publication Date
2025-10-31

AI Technical Summary

Technical Problem

Existing unmanned surface vessel (USV) and unmanned aerial vehicle (UAV) collaborative control systems struggle to accurately describe the motion characteristics of USVs in complex sea conditions. Their control algorithms are unable to handle highly nonlinear and strongly coupled sea-air collaborative control problems, lack adaptive learning capabilities, and thus cannot achieve true collaborative control.

Method used

A cooperative control method for unmanned surface vessels and unmanned aerial vehicles (UAVs) based on hydrodynamic parameters and deep reinforcement learning is proposed. This method acquires real-time information by carrying a communication module, constructs a dynamic model and linearly parameterizes it, combines an improved artificial potential field obstacle avoidance algorithm based on deep reinforcement learning, designs a network cooperative algorithm, optimizes potential field parameters and control gain, and uses energy function and input state stability theory for stability analysis.

Benefits of technology

It improves the stability and efficiency of collaborative control between unmanned surface vessels and unmanned aerial vehicles, enabling adaptive adjustment of control strategies in complex environments, avoiding control conflicts, achieving efficient collaborative operation and obstacle avoidance, and enhancing the system's adaptability and operational efficiency.

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Abstract

The invention belongs to the technical field of unmanned system control, and particularly discloses an unmanned ship and unmanned aerial vehicle system cooperative control method based on hydrodynamic parameters and deep reinforcement learning, electronic equipment and a readable storage medium, and the method comprises the steps: S1, obtaining the real-time position, speed and external environment information of each unmanned ship and unmanned aerial vehicle; s2, modeling the hydrodynamic system of each unmanned ship and unmanned aerial vehicle to obtain a dynamic model; s3, performing linear parameterization on the kinetic equations of each unmanned ship and each unmanned aerial vehicle; s4, constructing a deep reinforcement learning control algorithm for improving artificial potential field obstacle avoidance of the unmanned ship and the unmanned aerial vehicle; s5, constructing a network cooperation algorithm of the unmanned ship and the unmanned aerial vehicle; and S6, initializing hardware modules of each unmanned ship and each unmanned aerial vehicle, and loading a network cooperation algorithm into micro-control units of each unmanned ship and each unmanned aerial vehicle.
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