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.

翻译：本文报道了一种可部署的物理-数据混合动力学模型，用于多关节机械臂在多场景下的规划与操作。研究同时探索了基于物理模型与数据驱动模型，以选取最优规划模型。基于物理的模型通过拉格朗日方法构建，损失项包含惯性损失、粘性损失和摩擦损失。针对数据驱动模型，本研究探究了三种方法：深度神经网络、长短期记忆网络和极限梯度提升机。建模结果表明，经全面超参数优化后，XGBoost架构在精准表征机械臂动力学方面优于DNN和LSTM。综合物理模型与数据驱动模型的混合方法基于RMSE准则在所有模型中表现最优，且仅需约2.4万组训练数据。此外，我们利用从动力学模型推导的观测外力矩开发了机械臂虚拟力传感器，并基于物理-数据混合动力学模型设计了运动规划器。外力矩贡献于末端执行器上的力与力矩，便于与环境交互；内力矩则控制机械臂运动动力学并补偿内部损失。通过实测关节力矩与内部损失之差估算外力矩，我们实现了无传感器控制策略，并在轴孔装配任务中验证其有效性。最后，基于混合动力学模型的学习型运动规划器可协助机械臂规划时间最优轨迹。该综合方法凸显了将物理模型与数据驱动模型集成以实现工业环境中先进机械臂控制与规划的有效性。