Suitable trajectories with minimum execution time are essential for an industrial robot to enhance productivity in pick and place operations. A novel point-to-point trajectory planning algorithm (PTPA) is proposed to improve the motion efficiency of industrial robots. The jerk profile for a trajectory model is determined by five intervals and the jerk constraint. According to the kinematic constraints and two shape coefficients, a velocity threshold and three displacement thresholds are calculated for an individual joint to transfer the proposed jerk motion profile into four specific profiles. The optimal trajectory model of the joint is developed for the minimum-time and jerk-continuous trajectory via the performance evaluation with the input displacement and three displacement thresholds. Moreover, time-based motion synchronization for all joints is taken into account in PTPA to decrease unnecessary burdens on the actuators. The simulations illustrate that the execution time by PTPA is more efficient than that by other techniques. The experiments of a point-to-point application on a real six-axis industrial robot show that the absolute errors at the end of the motion for all joints are within 0.04°. These results prove that PTPA can be an effective point-to-point trajectory planner for industrial robots

Deep learning has made important contributions to classification tasks applied to fault diagnosis. However, it is crucial to integrate the technologies into real industrial applications through cost-effective hardware. Edge computing, a new computing paradigm, has the potential to accelerate system response time, reduce bandwidth for transmission, and use fewer computing resources. In this article, the distillation quantization compression method based on energy entropy is applied to compress the convolutional neural network (CNN), which is deployed on a Cortex-M4 series microcontroller with only 192 KB of RAM and 512 KB of ROM. Additionally, based on the fault mechanism of rolling bearings, this article integrates the attention mechanism and envelope spectrum to verify the effectiveness of feature extraction by the CNN model, which effectively weaken invalid features in distillation quantization process. The experimental results show that the proposed method has excellent performance in terms of memory overhead and inference speed, which has great potential in industrial applications.

Purpose: This paper aims to present an adaptive robust sliding mode tracking controller for a 6 degree-of-freedom industrial assembly robot with parametric uncertainties and external disturbances. The controller is used to achieve both stringent trajectory tracking, accurate parameter estimations and robustness against external disturbances.Design/methodology/approach: The controller is designed based on the combination of sliding mode control, adaptive and robust controls and hence has good adaptation and robustness abilities to parametric variations and uncertainties. The unknown parameter estimates are updated online based on a discontinuous projection adaptation law. The robotic dynamics is first formulated in both joint spaces and workspace of the robot’s end-effector. Then, the design procedure of the adaptive robust sliding mode tracking controller and the parameter update law is detailed.Findings: Comparative tests are also conducted to verify the effectiveness of the proposed controller, which show that the proposed controller achieves significantly better dynamic trajectory tracking performances as compared with conventional proportional derivative controller and sliding mode controller under the same conditions.Originality/value: This is a new innovation for industrial assembly robot to improve assembly automation.

With the growth of nonlinear electrical equipment, power quality disturbances (PQDs) often appear in electrical systems. To solve this, a practical heuristic methodology for PQD detection and classification based on empirical wavelet transform has been proposed. By using a multiresolution analysis tool, empirical wavelet transform, the voltage waveform signal is decomposed into several sub-signals, and some potential features are extracted in the statistical method. To reduce the feature vector dimensions, the ReliefF algorithm is used for feature selection and optimized for dimensionality reduction, which reduces the complexity of system calculation while ensuring accuracy. Finally, a classifier based on support vector machines (SVM) was built, and with the ranked feature vectors’ input, the PQD can be recognized. The experimental results verify that the classification results achieved high accuracy, which confirms the properties and robustness of the proposed approach in noisy environments.

SUMMARY: In recent years, soft robotics is widely considered as the most promising field for both research and application. First of all, the actuator is fundamental for designing, modeling, and controlling of soft robots. This paper presents a new type of pneumatic trunk-like soft actuator, which contains a chamber for stiffness adjustment in addition to three chambers for driving. Thus, the salient feature of the proposed actuator is the ability of stiffness self-regulation. The structure of the proposed actuator is described in detail. Then the theoretical models for elongation and bending motion of the actuator are established. The elongation as well as single-chamber and multi-chamber driving bending of the actuator were tested to verify the mathematical models. Finally, a dual-segment soft robot based on the proposed trunk-like soft actuator was developed and tested by experiments, which implies its potential application in practice.

Rolling bearing failures can lead to machinery breakdowns and loss of control, posing significant operational and safety risks. Traditional algorithms based on the empirical wavelet transform (EWT) face challenges in accurately extracting valid components under variable conditions. Additionally, conventional maxpooling methods often overlook critical features, while fully connected layers tend to overfitting due to excessive parameterization. To address these challenges, an enhanced algorithm is proposed, defined as the improved EWT-improved ResNet (IEWT-IResNet) method. Firstly, the vibration signals are preprocessed to enhance resilience under variable conditions. Secondly, an improved EWT (IEWT) is proposed with an optimal ridge-curve extraction strategy. And it incorporates an efficient weighted energy entropy (EWEE) as a sensitive indicator for signal reconstruction. Additionally, the 1D angular-serial signals are transformed into 2D image matrices using the generalized S-transform. Finally, an optimized residual network model is presented, incorporating singular value decomposition (SVD) pooling and global SVD pooling (GSP) strategies in the pooling and full connection layers. These enhancements benefits the optimal feature extraction while mitigating overfitting. The experimental results demonstrate the superior feature extraction capabilities of the proposed method when processing noisy and nonstationary signals. Furthermore, the effectiveness of the approach is investigated and validated using real-world data from rolling bearings.