Industrial robots have become indispensable machining equipment alongside machine tools due to their large workspace and high flexibility. However, their inherent structural compliance and geometric imperfections introduce spatially distributed pose errors, particularly in high-precision applications. Current robot error compensation is mostly based on single-source adjustment of the body, which is affected by the spatial sensitivity relationship between joint space and pose errors. For this reason, a hybrid manufacturing system integrating a robot and an external linear cell was constructed. Based on this system, a physical-informed approach for distributed prediction and collaborative compensation of robot machining quality is proposed. Firstly, a novel spatial–temporal attention-based sensing model was built to predict the robot pose errors. Secondly, an unsupervised distributed prediction module with physics informatization is constructed based on the sensitivity analysis of joint pose errors. Finally, the collaborative compensation is realized by a hybrid manufacturing system containing the robot itself and the external linear axes. Experimental validation on large-curvature surface machining demonstrates the system’s ability to simultaneously predict and compensate machining quality deviations, achieving positioning accuracy with maximum/average errors of 0.14 mm/0.03 mm respectively. The physics-based approach significantly outperforms conventional methods by coordinating distributed prediction with collaborative compensation, reducing sensitive joint adjustments while suppressing regenerative errors. These advancements establish a new paradigm for precision robotic machining in industrial applications.

For direct teleoperation tasks, the follower robot accomplishes tasks by strictly executing the inputs from the operator. However, the operator's physiological tremor seriously reduces the smoothness of the trajectory, especially in tasks relying on operator’s experience such as gluing, while the random tremor is hard to be described and suppressed online. To navigate this challenge, this paper proposes an Online Hidden Markov Model with Two-Layer Bayesian (TLB-OHMM) method to suppress the tremor by estimating the operator's expected speed, which can recognize new intention online and is adaptive to complex trajectories. First, the actual moving speed of the human hand is modeled as an HMM. Then, an Online-HMM method based on online expectation maximum (EM) algorithm is introduced to shorten the training time and realize the online updating of the HMM parameters. Finally, a two-layer Bayesian method is proposed to estimate the expected speed of the human hand. Experimental results in simulation and real teleoperated gluing task show that the proposed method greatly reduces computation time and improves the quality of trajectories, especially for complex curve trajectories, compared with the traditional HMM based method.

With the trend of lightweight in the field of intelligent electric vehicles and 3C, the demand for high precision machining of aluminum alloy parts is growing. And tool condition monitoring (TCM) is very important for quality control of parts, so intelligent high-accuracy wear prediction of aluminum alloy high precision machining tools has great industrial application value at present and in the future. This paper presents a novel TCM model (Conv-PhyFormer) of Transformer with physics informed. The model has excellent ability to capture short-term and long-term dependencies from nonlinear cutting time series data when there are few training samples. The embedded hard physical constraint and soft physical constraint in the model make the model partially interpretable. Soft physical constraint in the form of one-dimensional causal convolution can help the proposed model better learn the local context. Hard physical constraint in the form of the mathematical equation representing cutting physical knowledge are embedded, thus the model does not need to learn this knowledge from time series data from scratch. A large number of analysis results of aluminum alloy machining experimental data show that the proposed Conv-PhyFormer has significantly superior prediction accuracy and robustness compared with the current three popular deep learning models for TCM. Embedded soft and hard physical constraints can significantly reduce the training epochs of Transformer prediction model.