Dynamic characteristics and optimal design of the manipulator for automatic tool changer

In order to improve the reliability of changing tool for ATC (automatic tool changer), a horizontal tool changer of machining center is chosen as the example to study the dynamic characteristics in the condition of changing a heavy tool. This paper analyzes the structure and properties of the tool changer by simulation and experiment, and the space trajectory equations of the manipulator and tool are derived. The maximum force is calculated in the processing of changing tool. A virtual platform for the automatic tool changer is built to simulate and verify the dynamic performance of the tool changer; the simulation results show an obvious vibration in the process of changing tool, which increases the probability of failure for changing tool. Moreover, in order to find out the device's vibration reasons, a professional experiment platform is built to test the dynamic characteristics. Based on the testing results for a horizontal tool changer, it is known that the unstable vibration is mainly caused by the collision of the tool. Finally, an optimization method for the manipulator is proposed to reduce this vibration and improve the reliability of the tool changer. The final simulation and experiment results show that the optimized manipulator can grasp the heavy tool stably, and the vibration amplitude is significantly reduced in the process of changing tool

Context De-confounded Emotion Recognition

Context-Aware Emotion Recognition (CAER) is a crucial and challenging task that aims to perceive the emotional states of the target person with contextual information. Recent approaches invariably focus on designing sophisticated architectures or mechanisms to extract seemingly meaningful representations from subjects and contexts. However, a long-overlooked issue is that a context bias in existing datasets leads to a significantly unbalanced distribution of emotional states among different context scenarios. Concretely, the harmful bias is a confounder that misleads existing models to learn spurious correlations based on conventional likelihood estimation, significantly limiting the models' performance. To tackle the issue, this paper provides a causality-based perspective to disentangle the models from the impact of such bias, and formulate the causalities among variables in the CAER task via a tailored causal graph. Then, we propose a Contextual Causal Intervention Module (CCIM) based on the backdoor adjustment to de-confound the confounder and exploit the true causal effect for model training. CCIM is plug-in and model-agnostic, which improves diverse state-of-the-art approaches by considerable margins. Extensive experiments on three benchmark datasets demonstrate the effectiveness of our CCIM and the significance of causal insight.Comment: Accepted by CVPR 2023. CCIM is available at https://github.com/ydk122024/CCI