Unmanned aerial vehicle model prediction control method, system and device based on Actor-Critic and artificial potential field and medium

By introducing the Actor-Critic algorithm and artificial potential field in the UAV control, the efficiency and real-time problems of UAV flight control in complex environments are solved, and efficient path planning and prediction tracking are achieved.

CN119645094AActive Publication Date: 2025-03-18XIDIAN UNIV
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Patent Information

Application Number
CN202411289231.9
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2024-09-14
Publication Date
2025-03-18
Estimated Expiration
2044-09-14

AI Technical Summary

Technical Problem

The existing UAV control technology is difficult to achieve efficient and intelligent flight control in complex and changing environments, and the calculation complexity of model prediction control is high, the real-time performance is poor, and the reward function design efficiency in reinforcement learning is low, resulting in slow network update convergence speed.

Method used

The drone model prediction and control method based on Actor-Critic and artificial potential fields is adopted. By introducing artificial potential fields, the environment perception and path planning capabilities of the drone are enhanced, the reward function is redesigned, and the advantages of model prediction control and reinforcement learning are combined to achieve efficient path planning and prediction tracking of the drone in complex environments.

Benefits of technology

It improves the flight efficiency and accuracy of path planning of drones in complex environments, reduces the computing complexity and network update time, and improves the real-time and robustness of the system.

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Abstract

The invention discloses an unmanned aerial vehicle model prediction control method, system and device based on Actor-Critic and an artificial potential field, and a medium. A network of multiple static multi-obstacle environments and dynamic multi-obstacle environments, a kinetic model of a four-rotor unmanned aerial vehicle, an award value based on the artificial potential field, and an unmanned aerial vehicle model prediction control algorithm based on Actor-Critic and the artificial potential field is constructed in sequence; training an algorithm model according to the set network parameters, and selecting an optimal weight file; inputting the optimal weight file and a network of an unmanned aerial vehicle model prediction control algorithm based on Actor-Critic and an artificial potential field into a plurality of static multi-obstacle environments and dynamic multi-obstacle environments for testing; obtaining an unmanned aerial vehicle flight path diagram, a reward value r, a path length L, a path maximum torsion degree LS, a path global torsion degree GS, and an index result of whether collision occurs or not and threat; the system, the equipment and the medium are used for implementing the method. The method can effectively plan the path and timely avoid the obstacle, and has the advantages of high real-time performance, strong robustness, timely obstacle avoidance and the like.
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