超冗余电缆驱动柔性机械手的智能运动物理引擎结构
Intelligent kinematic physics engine construction for hyper-redundant cable-driven flexible manipulator.
摘要(中文译文)
为解决超冗余度绳索驱动柔性机械臂运动学建模困难与实时性差的问题，本文提出一种基于贝叶斯优化长短期记忆（BO-LSTM）网络的智能运动学物理引擎构建方法。首先，基于Denavit-Hartenberg（D-H）参数法建立柔性机械臂的多级运动学方程，并建立正解解析模型。其次，基于运动学方程提出牛顿迭代法的逆运动学模型，并证明数值求解方法的稳定性。随后，基于蒙特卡洛方法、正解解析模型与逆解高精度数值模型建立运动学数据库，并对数据库进行可视化。最后，基于运动学数据库与BO-LSTM神经网络模型建立智能运动学物理引擎。该模型结合了贝叶斯优化的全局搜索能力与LSTM神经网络在处理时间序列数据方面的优势，为超冗余度绳索驱动柔性机械臂的运动学建模提供了新的解决方案。正解与逆解BO-LSTM模型的计算速度分别为0.018 s和0.017 s，快于数值方法。正解与逆解BO-LSTM模型的均方误差分别为0.065和0.056，低于BP、RBF和LSTM神经网络。利用MATLAB中的SimMechanics模块对超冗余度绳索驱动柔性机械臂进行仿真，实验结果表明，基于BO-LSTM神经网络模型的智能运动学物理引擎在运动学求解方面具有高精度和高效率。
Abstract
To solve the problems of difficult kinematics modeling and poor real-time performance for a hyper-redundant cable-driven flexible manipulator, a construction method of an intelligent kinematics physics engine based on Bayesian-optimized Long Short-Term Memory (BO-LSTM) network is proposed in this paper. Firstly, the multilevel kinematics equation of the flexible manipulator is established based on the Denavit-Hartenberg (D-H) parameter method, and an analytical model of the forward solution is established. Secondly, the inverse kinematic model of the Newton iteration method is proposed based on the kinematics equation, and the stability of the numerical solution method is proven. Subsequently, the kinematics database is established based on the Monte Carlo method, the analytical model of forward solution and the high-precision numerical model of inverse solution, and the database is visualized. Finally, an intelligent kinematics physics engine is established based on the kinematics database and the BO-LSTM neural network model. This model combines the global search ability of Bayesian optimization and the advantages of LSTM neural network in processing time series data, which provides a new solution for the kinematic modeling of hyper-redundant cable-driven flexible manipulator. The calculation speed of the forward and inverse BO-LSTM models are 0.018 s and 0.017 s, which are faster than the numerical method. The MSEs of the forward and inverse BO-LSTM models are 0.065 and 0.056 respectively, which are lower than those of the BP, RBF and LSTM neural networks. The SimMechanics module in MATLAB is used to simulate the hyper-redundant cable-driven flexible manipulator. The experimental result shows that the intelligent kinematics physics engine based on the BO-LSTM neural network model has high precision and high efficiency in kinematics solution.
如何引用
AMA
Songtao Wang, Peiang Wang, Xueqian Wang, Sisi Chen, Mingwei Liu. Intelligent kinematic physics engine construction for hyper-redundant cable-driven flexible manipulator.. PloS one. 2026; doi:10.1371/journal.pone.0353108.
APA
Songtao Wang, Peiang Wang, Xueqian Wang, Sisi Chen, Mingwei Liu (2026). Intelligent kinematic physics engine construction for hyper-redundant cable-driven flexible manipulator.. PloS one. https://doi.org/10.1371/journal.pone.0353108
GB/T 7714
Songtao Wang, Peiang Wang, Xueqian Wang, Sisi Chen, Mingwei Liu. Intelligent kinematic physics engine construction for hyper-redundant cable-driven flexible manipulator.[J]. PloS one, 2026 doi:10.1371/journal.pone.0353108.
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