This study addresses the problem in designing pneu‐net actuators to achieve specific target trajectories for particular applications. The finite element analysis (FEA) method is used to study the sensitivity of 9 design parameters to bending deformation of actuators, and the results of FEA demonstrate that the width of the chamber and the width of the middle layer have great influence on the bending performance. Besides, the relations between the bending angles of actuators with different width parameters and the pressure are always highly linear. Using FEA and the cubic polynomial fitting algorithm, a unified bending model of the pneu‐net actuator is established with three crucial design parameters (i.e., segment length, chamber width, and middle‐layer width). An optimum design method based on the bending model and genetic algorithm (GA) is put forward to automatically adjust these three crucial parameters to realise trajectory matching. The method is developed to be an effective solution for the problem in matching the target trajectory with the experimental results achieved on an actuator designed by imitating a human index finger. Furthermore, the results of the experiment also verify that the actuator can achieve stable grasping, and the proposed method has an important directive to design pneu‐net actuators for their application in the fields of robotics, rescue, and detection.

This study describes the design of a novel flexible robotic hand that can adapt its configurations to different grasping demands. Firstly, a mathematical model, based on the Yeoh strain energy function and virtual work principle, is established to investigate deformation properties of the designed soft finger. To achieve a flexible grasping capability, a changeable palm is presented with its variable configurations in terms of target objects with different sizes and shapes. A kinematic model of the flexible robotic hand is established, and then the numerical simulations based on the Monte-Carlo method and Matlab is applied to analyse the workspace of the hand and address the parameter optimisation problem of the rigid-flexible coupled system. Furthermore, an optimised grasping strategy on the basis of the principle of optimal efficiency is proposed to obtain an optimal grasping pose for the target object. Finally, a prototype is developed and tested in a laboratory to demonstrate the feasibility and effectiveness of our proposed hand. The results of practical experiments show that the robotic hand cannot only stably grasp objects with different sizes and shapes but also flexibly manipulate soft and fragile ones.

This study addresses the problem of nonlinear error predictive compensation to achieve high positioning accuracy for advanced industrial applications. An improved calibration method based on the generalisation performance evaluation is proposed to enhance the stability and accuracy of robot calibration. With the development of technology, a deep neural network (DNN) optimised by a genetic algorithm (GA) is applied to predict the nonlinear error of the calibrated robot. To address the change of external payload, an extra compliance error model is established with a linear piecewise method. A global compensation method combining the GA-DNN nonlinear regression prediction model and the compliance error model is then proposed to achieve the robot’s high-precision positioning performance under any external payload. Experimental results obtained on a Staubli RX160L robot with a FARO laser tracker are introduced to demonstrate the effectiveness and benefits of our proposed methodology. The enhanced positioning accuracy can reach 0.22 mm with 98% probability (i.e., the maximum positioning error in all test data).

Remaining useful life (RUL) prediction of equipment has important significance for guaranteeing production efficiency, reducing maintenance cost, and improving plant safety. This paper proposes a novel method based on an new particle filter (PF) for predicting equipment RUL. Genetic algorithm (GA) is employed to improve the particle leanness problem that arises in traditional PF algorithms, and a time-varying auto regressive (TVAR) model and Akaike Information Criterion (AIC) are integrated to establish the dynamic model for PF. Moreover, starting prediction time (SPT) detection method based on hypothesis testing theory is presented, by which SPT of equipment RUL can be adaptively detected. In order to verify the effectiveness of the methods proposed in this study, a simulation test and the accelerating fatigue test of a rolling element bearing are designed for RUL prediction. The test results show the methods proposed in this study can accurately predict the RUL of the rolling element bearing, and it performs better than the traditional PF algorithm and support vector machine (SVM) in the RUL prediction.