Version 1
: Received: 29 August 2024 / Approved: 31 August 2024 / Online: 2 September 2024 (13:13:56 CEST)
How to cite:
Xu, B.; Zhang, X.; Yu, X.; Ou, Y.; Zhang, K.; Cai, H.; Zhao, J.; Fan, J. An Implementation based on Mask-D3QN of Quadruped Robot Motion for Steam Generator. Preprints2024, 2024090035. https://doi.org/10.20944/preprints202409.0035.v1
Xu, B.; Zhang, X.; Yu, X.; Ou, Y.; Zhang, K.; Cai, H.; Zhao, J.; Fan, J. An Implementation based on Mask-D3QN of Quadruped Robot Motion for Steam Generator. Preprints 2024, 2024090035. https://doi.org/10.20944/preprints202409.0035.v1
Xu, B.; Zhang, X.; Yu, X.; Ou, Y.; Zhang, K.; Cai, H.; Zhao, J.; Fan, J. An Implementation based on Mask-D3QN of Quadruped Robot Motion for Steam Generator. Preprints2024, 2024090035. https://doi.org/10.20944/preprints202409.0035.v1
APA Style
Xu, B., Zhang, X., Yu, X., Ou, Y., Zhang, K., Cai, H., Zhao, J., & Fan, J. (2024). An Implementation based on Mask-D3QN of Quadruped Robot Motion for Steam Generator. Preprints. https://doi.org/10.20944/preprints202409.0035.v1
Chicago/Turabian Style
Xu, B., Jie Zhao and Jizhuang Fan. 2024 "An Implementation based on Mask-D3QN of Quadruped Robot Motion for Steam Generator" Preprints. https://doi.org/10.20944/preprints202409.0035.v1
Abstract
Crawling robots are the focus of intelligent inspection research, and the main feature of this type of robot is the flexibility of in-plane attitude adjustment. The crawling robot HIT_Spibot is a new type of steam generator heat transfer tube inspection robot with a unique mobility capability different from traditional quadrupedal robots. This paper introduces a hierachical motion planning approach for HIT_Spibot, aiming to achieve efficient and agile maneuverability. The proposed method integrates three distinct planners to handle complex motion tasks: a nonlinear optimization-based base motion planner, a TOPSIS-based base orientation planner, and a Mask-D3QN (MD3QN) algorithm-based gait motion planner. Initially, the robot's base and foot workspace are delineated through envelope analysis, followed by trajectory computation using Larangian methods. Subsequently, the TOPSIS algorithm is employed to establish an evaluation framework conducive to foundational turning planning. Finally, the MD3QN algorithm trains foot points to facilitate robot movement along predefined paths. Experimental results demonstrate the method's adaptability across diverse tube structures, showcasing robust performance even in environments with random obstacles. Compared to the D3QN algorithm, MD3QN achieves a 100% success rate, enhances average overall scores by 6.27%, reduces average stride lengths by 39.04%, and attains a stability rate of 58.02%. These results not only validate the effectiveness and practicality of the method but also showcase the significant potential of HIT_Spibot in the field of industrial inspection.
Keywords
crawling robot; deep reinforcement learning; motion planning; hierarchical planning
Subject
Engineering, Mechanical Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
