Mechanical arm trajectory planning method and device based on reinforcement learning, equipment and medium

By using a reinforcement learning-based approach to dynamically select a trajectory planning model and utilize a historical trajectory database, the problems of low efficiency and poor adaptability in robotic arm trajectory planning are solved, achieving efficient and accurate trajectory planning.

CN120962646APending Publication Date: 2025-11-18王硕
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Patent Information

Application Number
CN202511079051.2
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-08-02
Publication Date
2025-11-18

AI Technical Summary

Technical Problem

Existing robotic arm trajectory planning methods suffer from low computational efficiency, poor dynamic adaptability, difficulty in real-time path adjustment, and slow response to complex environmental changes.

Method used

A reinforcement learning-based approach is adopted to dynamically select a trajectory planning reinforcement learning model by acquiring the robot arm's pose parameters and environmental obstacle distribution label information in real time, and using the historical trajectory database to identify transferable trajectory information and generate real-time trajectories.

Benefits of technology

It improves the efficiency and adaptability of trajectory planning, reduces online computation, enhances the model's generalization ability and multi-objective optimization capability of trajectory planning, and ensures trajectory accuracy and real-time performance.

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Abstract

The invention relates to a mechanical arm trajectory planning method and device based on reinforcement learning, equipment and a medium. The method comprises the following steps: acquiring a real-time pose parameter, a target pose parameter and a state transition parameter of the mechanical arm; on the basis of the real-time pose parameter, the state transition parameter and the target pose parameter, transferable trajectory information is identified from a historical trajectory database; environment obstacle distribution label information of the mechanical arm is obtained, and a trajectory planning reinforcement learning model of the mechanical arm is selected based on the environment obstacle distribution label information; and the transferable trajectory information is input into a trajectory planning reinforcement learning model matched with the environment obstacle distribution label information, and real-time trajectory information of the mechanical arm is generated. By the adoption of the method, the defects of traditional trajectory planning in dynamic environment adaptability, calculation efficiency and generalization ability can be overcome by fusing real-time sensing, historical data migration and dynamic model matching, and efficient and safe operation of the mechanical arm in an unstructured scene can be achieved.
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