Abstract
We report on the development of an implementable physics-data hybrid dynamic model for an articulated manipulator to plan and operate in various scenarios. Meanwhile, the physics-based and data-driven dynamic models are studied in this research to select the best model for planning. The physics-based model is constructed using the Lagrangian method, and the loss terms include inertia loss, viscous loss, and friction loss. As for the data-driven model, three methods are explored, including DNN, LSTM, and XGBoost. Our modeling results demonstrate that, after comprehensive hyperparameter optimization, the XGBoost architecture outperforms DNN and LSTM in accurately representing manipulator dynamics. The hybrid model with physics-based and data-driven terms has the best performance among all models based on the RMSE criteria, and it only needs about 24k of training data. In addition, we developed a virtual force sensor of a manipulator using the observed external torque derived from the dynamic model and designed a motion planner through the physics-data hybrid dynamic model. The external torque contributes to forces and torque on the end effector, facilitating interaction with the surroundings, while the internal torque governs manipulator motion dynamics and compensates for internal losses. By estimating external torque via the difference between measured joint torque and internal losses, we implement a sensorless control strategy which is demonstrated through a peg-in-hole task. Lastly, a learning-based motion planner based on the hybrid dynamic model assists in planning time-efficient trajectories for the manipulator. This comprehensive approach underscores the efficacy of integrating physics-based and data-driven models for advanced manipulator control and planning in industrial environments.
Abstract (translated)
我们报道了一个可实施物理学数据混合动态模型的发展，用于设计和管理一个关节式操作器在各种场景下的运动规划和操作。同时，本研究还探讨了基于物理和数据驱动的动态模型的研究，以选择最佳模型进行规划。基于物理的模型使用拉格朗日方法构建，其中包括惯性损失、粘滞损失和摩擦损失。对于数据驱动模型，我们探讨了包括DNN、LSTM和XGBoost三种方法。我们模型的研究结果表明，在全面优化超参数后，XGBoost架构在准确表示操作器动力方面优于DNN和LSTM。基于物理和数据驱动的混合模型在所有基于RMSE标准的模型中具有最佳性能，而且只需要约24k的训练数据。此外，我们还使用从动态模型中观察到的外部扭矩开发了一个操作器的虚拟力传感器，并通过物理数据混合动态模型设计了一个运动规划器。外部扭矩对末端执行器的力和扭矩产生贡献，促进与周围环境的交互，而内部扭矩控制操作器运动动态并抵消内部损失。通过通过测量关节扭矩与内部损失之差估算外部扭矩，我们实现了无需传感器即可控制的策略，并通过一个钉孔任务证明了其有效性。最后，基于混合动态模型的学习运动规划器有助于为操作器在工业环境中的高级控制和规划实现高效的时间轨迹规划。这种全面的方法突出了将物理学基础和数据驱动模型整合起来在工业环境中设计高级操作器控制和规划的有效性。
URL
https://arxiv.org/abs/2405.04503
