Deep reinforcement learning driven crane trajectory planning method

By employing a deep reinforcement learning-driven crane trajectory planning method, a crane operation state space and motion space are constructed. A dense reward function is designed, and the Actor-Critic network and HER experience playback strategy are improved. This solves the problem of traditional cranes relying on driver experience, realizes autonomous loading and unloading operations of cranes, and enhances safety and intelligence levels.

CN122221699AActive Publication Date: 2026-06-16SHANDONG UNIV
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Patent Information

Application Number
CN202610684955.6
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2026-05-19
Publication Date
2026-06-16
Estimated Expiration
2046-05-19

AI Technical Summary

Technical Problem

Traditional crane control relies on the operator's experience, making it difficult to guarantee safety. In particular, it lacks autonomous trajectory planning capabilities in complex, changeable, or high-risk operating environments, making it difficult to achieve intelligent operation.

Method used

A deep reinforcement learning-driven crane trajectory planning method is adopted to construct the crane's operation state space and action space, design a dense reward function, and improve the Actor-Critic network architecture and HER experience playback strategy to achieve autonomous loading and unloading operations.

Benefits of technology

Reducing human intervention improves the real-time performance and adaptability of crane operations, thus promoting the intelligentization of crane operations.

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Abstract

The invention belongs to the field of crane trajectory planning, and particularly relates to a crane trajectory planning method driven by deep reinforcement learning, and the method is characterized in that a crane operation state space, an action space and a dense reward function are designed; an improved Actor-Critic network architecture is constructed, and meanwhile, an HER experience playback strategy is improved so as to more effectively utilize failure track learning; the method has the advantages that strategies can be adjusted in real time according to load changes and environment changes, and the method adapts to complex and changeable working conditions of ports. The intelligent process of crane operation is promoted, the influence of human factors is reduced, and the real-time performance and the self-adaptability are high.
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