Adaptive planner parameter tuning method and device based on reinforcement learning

By introducing a hierarchical architecture and error compensator into the adaptive planner parameter tuning method, the problem of control layer limitation in complex environments is solved, and more efficient and reliable mobile robot navigation is achieved.

CN119644733AActive Publication Date: 2025-03-18ZHEJIANG UNIV
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Patent Information

Application Number
CN202411760008.8
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2024-12-03
Publication Date
2025-03-18
Estimated Expiration
2044-12-03

AI Technical Summary

Technical Problem

The existing adaptive planner parameter tuning method is difficult to completely solve the limitations of the control layer in complex environments, resulting in limited tracking errors and system performance.

Method used

Adaptive planner parameter tuning method based on reinforcement learning is adopted, and the hierarchical architecture of low-frequency parameter tuning, medium-frequency planning and high-frequency control is designed, and the tracking error is corrected in real time by combining the error compensator to achieve continuous improvement of system performance.

Benefits of technology

Effectively reduce tracking errors, improve the system's robustness and navigation efficiency, enhance the system's obstacle avoidance capabilities, and achieve more accurate, efficient and reliable mobile robot navigation tasks execution.

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Abstract

The invention discloses an adaptive planner parameter tuning method and device based on reinforcement learning, and the method comprises the steps: employing a reinforcement learning algorithm to adaptively carry out the parameter adjustment of a local planner according to the input environment data in a parameter adjustment network under a low-frequency control frequency; under the medium-frequency control frequency, generating a planning track of robot advancing according to the adjusted parameters in the local planner, and generating a feed-forward speed according to the planning track; under the high-frequency control frequency, a final control instruction is generated in the controller according to the feed-forward speed and the feedback speed calculated by the error compensator; and iteratively and alternately training and testing the parameter adjustment network and the controller, and continuously adjusting and optimizing the parameters of the local planner and a final control instruction generated by the controller according to a test result. According to the invention, the robustness and navigation efficiency of the system can be improved, the tracking error is effectively reduced, the obstacle avoidance capability is enhanced, and a more accurate, efficient and reliable robot navigation task can be completed in a complex environment.
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