Xu Biying, Zhang Xuehe, Yu Xuan, Ou Yue, Zhang Kuan, Cai Hegao, Zhao Jie, Fan Jizhuang
State Key Laboratory of Robotics and System, Harbin Institute of Technology, Harbin 150001, China.
Biomimetics (Basel). 2024 Sep 30;9(10):592. doi: 10.3390/biomimetics9100592.
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 hierarchical 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 were delineated through envelope analysis, followed by trajectory computation using Larangian methods. Subsequently, the TOPSIS algorithm was employed to establish an evaluation framework conducive to foundational turning planning. Finally, the MD3QN algorithm trained foot-points to facilitate robot movement along predefined paths. Experimental results demonstrated the method's adaptability across diverse tube structures, showcasing robust performance even in environments with random obstacles. Compared to the D3QN algorithm, MD3QN achieved a 100% success rate, enhanced average overall scores by 6.27%, reduced average stride lengths by 39.04%, and attained 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.
爬行机器人是智能检测研究的重点,这类机器人的主要特点是平面姿态调整的灵活性。爬行机器人HIT_Spibot是一种新型的蒸汽发生器传热管检测机器人,具有与传统四足机器人不同的独特移动能力。本文介绍了一种针对HIT_Spibot的分层运动规划方法,旨在实现高效灵活的机动性。所提出的方法集成了三种不同的规划器来处理复杂的运动任务:基于非线性优化的基础运动规划器、基于TOPSIS的基础方向规划器和基于Mask-D3QN(MD3QN)算法的步态运动规划器。首先,通过包络分析划定机器人的基座和足部工作空间,然后使用拉格朗日方法进行轨迹计算。随后,采用TOPSIS算法建立有利于基础转向规划的评估框架。最后,MD3QN算法对脚点进行训练,以促进机器人沿预定路径移动。实验结果表明,该方法在不同的管道结构中具有适应性,即使在存在随机障碍物的环境中也表现出强大的性能。与D3QN算法相比,MD3QN的成功率达到100%,平均总分提高了6.27%,平均步长减少了39.04%,稳定率达到58.02%。这些结果不仅验证了该方法的有效性和实用性,也展示了HIT_Spibot在工业检测领域的巨大潜力。