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The accurate prediction of driving torque demand is essential for the development of motion controllers for mobile robots on complex terrains. This paper aims to propose a hybrid model of torque prediction, adaptive EC-GPR, for mobile robots to address the problem of estimating the required driving torque with unknown terrain disturbances.

An error compensation (EC) framework is used, and the preliminary prediction driving torque value is achieved using Gaussian process regression (GPR). The error is predicted using a continuous hidden Markov model to generate compensation for the prediction residual caused by terrain disturbances and uncertainties. As the final step, a gain coefficient is used to adaptively tune the significance of the compensation term through parameter resetting. The proposed model is verified on a sample set, including the driving torque of a mobile robot on three different sandy terrains with two driving modes.

The results show that the adaptive EC-GPR yields the highest prediction accuracy when compared with existing methods.

It is demonstrated that the proposed model can predict the driving torque accurately for mobile robots in an unconstructed environment without terrain identification.

The purpose of the paper is to propose an efficient and accurate force/torque (F/T) sensing method for the robotic wrist-mounted six-dimensional F/T sensor based on an excitation trajectory.

This paper presents an efficient and accurate F/T sensing method based on an excitation trajectory. First, the dynamic identification model is established by comprehensively considering inertial forces/torques, sensor zero-drift values, robot base inclination errors and forces/torques caused by load gravity. Therefore, the sensing accuracy is improved. Then, the excitation trajectory with optimized poses is used for robot following and data acquisition. The data acquisition is not limited by poses and its time can be significantly shortened. Finally, the least squares method is used to identify parameters and sense contact forces/torques.

Experiments have been carried out on the self-developed robot manipulator. The results strongly demonstrate that the proposed approach is more efficient and accurate than the existing widely-adopted method. Furthermore, the data acquisition time can be shortened from more than 60 s to 3 s/20 s. Thus, the proposed approach is effective and suitable for fast-paced industrial applications.

The main contributions of this paper are as follows: the dynamic identification model is established by comprehensively considering inertial forces/torques, sensor zero-drift values, robot base inclination errors and forces/torques caused by load gravity; and the excitation trajectory with optimized poses is used for robot following and data acquisition.

This paper aims at the analytical formulation of the electromagnetic features of flux switching permanent magnet (PM) machines with emphasis on the PM air gap flux density and armature magnetic reaction.

The PM air gap flux density is formulated considering three different analytical models. These differ by the incorporation of the air gap magnetic saliency level from the stator side. In addition, the armature magnetic reaction is investigated based on a simplified magnetic reluctance circuit that considers the flux switching permanent magnet machines magnetic circuit geometry specification. Then, the no- and on-load torque is predicted based on the two air gap flux densities.

It has been found that the PM air gap flux density considering the stator saliencies with trapezoidal magnetomotive force waveform presents the highest accuracy. Despite the simplicity of the magnetic equivalent circuit-based approach, the predicted air gap armature magnetic reaction is in good agreement with the finite element analysis (FEA) one. These lead to the analytical predictions of the no- and on-load torque which is characterized by an acceptable accuracy.

This work should be extended to experimental validation of the FEA results regarding the torque production generation.

The paper proposes an improved design-oriented analytical approach with emphasis on the PM air gap flux density and the armature magnetic reaction of flux switching PM machines.

The purpose of this paper is to actively calibrate power density to match the application requirements with as small an actuator as possible. So, this paper introduces shape memory alloy to design variable stiffness elements. Meanwhile, the purpose of this paper is also to solve the problem of not being able to install sensors on shape memory alloy due to volume limitations.

This paper introduces the design, modeling and control process for a variable stiffness passive ankle exoskeleton, adjusting joint stiffness using shape memory alloy (SMA). This innovative exoskeleton aids the human ankle by adapting the precompression of elastic components by SMA, thereby adjusting the ankle exoskeleton’s integral stiffness. At the same time, this paper constructs a mathematical model of SMA to achieve a dynamic stiffness adjustment function.

Using SMA as the driving force for stiffness modification in passive exoskeletons introduces several distinct advantages, inclusive of high energy density, programmability, rapid response time and simplified structural design. In the course of experimental validation, this ankle exoskeleton, endowed with variable stiffness, proficiently executed actions like squatting and walking and it can effectively increase the joint stiffness by 0.2 Nm/Deg.

The contribution of this paper is to introduce SMA to adjust the stiffness to actively calibrate power density to match the application requirements. At the same time, this paper constructs a mathematical model of SMA to achieve a dynamic stiffness adjustment function.

By analyzing the shortcomings of existing insulator robots, a novel ultra high voltage (UHV) insulator climbing robot, which could transfer between adjacent insulator strings, is proposed for operation on 800KV multiple-string insulators. An extended inchworm-like configuration was chosen and a stable gripping claw suitable for the insulator string was designed to enable the robot to multiple-string insulators. Then a set of nonheuristic structural parameters that can influence energy consumption was chosen to formulate a nonlinear optimization problem based on the configuration, which improved the energy efficiency of the robot during progressing along a string of insulator.

The purpose of this paper is to design an insulator climbing robot for operation on UHV multiple-string insulators, which could transfer between adjacent insulator strings and progressed along a string of insulator with high energy efficiency.

A physical prototype was constructed that can operate at the speed of six pieces per minute (approximately 1.44 meters per minute) on a single string and complete transference between adjacent strings in 45 s. The energy consumption of joints during progressed along a string of insulator had been reduced by 38.8% with the optimized parameters, demonstrating the consistency between the experimental and simulation results.

An insulator climbing robot for operation on UHV multiple-string insulators has been developed with energy consumption optimization design. The robot can transfer between adjacent insulator strings and progressed along a string of insulator with high energy efficiency. The CLIBOT could be expanded to detect or clean the insulators with similar specification.

The purpose of this study is to reduce the cogging torque in axial flux permanent magnet (AFPM) machine using optimal magnet shape.

This study analyzes different magnet shapes for AFPM machine performance enhancement. Three-dimensional (3D) finite element analysis is performed to see the effects of pole shaping on the cogging torque of the AFPM machine.

The magnetic pole shape has a significant effect on cogging torque and overall efficiency. The conventional model has the highest torque whereas the conventional skewing affected cogging torque positively and significantly reduced the cogging torque. The combination of skewing the pole along with face curving is more effective and decreases the cogging torque from 3.88 Nm to 1.5 Nm.

Rare-earth magnets are the most expensive and important part of AFPM machines. Shape and volume optimization of rare-earth magnets is crucial for the performance of AFPM machines. The research aims to analyze the different permanent magnet designs for performance improvement of the AFPM machine. Conventional flat top trapezoidal, curved-top and skewed-magnet shapes are analyzed and the performance of the AFPM machine is compared with different magnet shapes. Curved-top shape and skewed magnet significantly reduce the cogging torque. Furthermore, a combination of curved-top shape and skew magnet shape is proposed to reduce the cogging torque further and improve the AFPM machine’s overall performance. Newly proposed magnet profile gives skewed curve magnet shapes which reduce the cogging torque further. 3D finite element analysis has been used to analyze the single-sided AFPM with all four different magnet shapes. The research focuses on single-sided AFPM machines, but the results are also valid for double-sided AFPM machines and can be extended to other topologies of AFPM machines.

This paper aims to deal with the force and position tracking problem when a robot performs a task in interaction with an unknown environment and presents a hybrid control strategy based on variable admittance control and fixed-time control.

A hybrid control strategy based on variable admittance control and fixed-time control is presented. Firstly, a variable stiffness admittance model control based on proportional integral and differential (PID) is adopted to maintain the expected force value during the task execution. Secondly, a fixed-time controller based on radial basis function neural network (RBFNN) is introduced to handle the model uncertainties and ensure the fast position tracking convergence of the robot system, while the singularity problem is also avoided by designing the virtual control variable with piecewise function.

Simulation studies conducted on the robot manipulator with two degrees of freedom have verified the superior performance of the proposed control strategy comparing with other methods.

A hybrid control scheme for robot–environment interaction is presented, in which the variable stiffness admittance method is adopted to adjust the interaction force to the desired value, and the RBFNN-based fixed-time position controller without singularity problem is designed to ensure the fast convergence of the robot system with model uncertainty.

This study aims to understand the influence of raceway surface topography on the temperature rise characteristics of silicon nitride (Si₃N₄) full ceramic ball bearing and improve its service life.

The arithmetic average height S_a, skewness S_sk and kurtosis S_ku in the three-dimensional surface roughness parameters are used to quantitatively characterize the surface topography of the raceway after superfinishing. The bearing life testing machine is used to test the Si₃N₄ full ceramic ball bearing using polytetrafluoroethylene (PTFE) cage under dry friction conditions, and the self-lubricating full ceramic ball bearing heat generation model is established.

With the decrease of S_a and S_sk on the raceway surface and the increase of S_ku, the average height of the raceway surface decreases, and the peaks and valleys tend to be symmetrically distributed on the average surface, and the surface texture becomes tighter. This kind of raceway surface topography is beneficial to form a thin and uniform filamentous PTFE transfer film with a wide coverage area on the raceway surface based on consuming less cage materials and improving the temperature rise characteristics of hot isostatic pressing silicon nitride full ceramic ball bearings.

The research results provide a theoretical basis for the reasonable selection of Si₃N₄ ring raceway processing technology and have important significance for improving the working characteristics and service life of Si₃N₄ full ceramic ball bearings under dry friction conditions.

This study aims to improve the robot’s performance during interactions with human and uncertain environments.

A joint stiffness model was established according to the molecular current method and the virtual displacement method. The position and stiffness coordination controller and fuzzy adaptive controller of variable stiffness joint are designed, and the principle prototype of variable stiffness joint is built. The position step and trajectory tracking performance of the variable stiffness joint are verified through experiments.

Experimental test shows that the joint stiffness can be quickly adjusted. The accuracy of position and trajectory tracking of the joint increases with higher stiffness and decreases with increasing frequency. The fuzzy adaptive controller performed better than the position and stiffness coordination controller in controlling the position step and trajectory tracking of the variable stiffness joint.

A hybrid flux adjustment mechanism is proposed for the components of variable stiffness robot joints, which reduces the mass of the output end of variable stiffness joints and the speed of joint stiffness adjustment. Aiming at the change of system controller performance caused by the change of joint stiffness, a fuzzy adaptive controller is proposed to improve the position step and trajectory tracking characteristics of variable stiffness joints.

The aim of this paper is to review the literature on the prediction of cutting tool life. Tool life is typically estimated by predicting the time to reach the threshold flank wear width. The cutting tool is a crucial component in any machining process, and its failure affects the manufacturing process adversely. The prediction of cutting tool life by considering several factors that affect tool life is crucial to managing quality, cost, availability and waste in machining processes.

This study has undertaken the critical analysis and summarisation of various techniques used in the literature for predicting the life or remaining useful life (RUL) of the cutting tool through monitoring the tool wear, primarily flank wear. The experimental setups that comprise diversified machining processes, including turning, milling, drilling, boring and slotting, are covered in this review.

Cutting tool life is a stochastic variable. Tool failure depends on various factors, including the type and material of the cutting tool, work material, cutting conditions and machine tool. Thus, the life of the cutting tool for a particular experimental setup must be modelled by considering the cutting parameters.

This submission discusses tool life prediction comprehensively, from monitoring tool wear, primarily flank wear, to modelling tool life, and this type of comprehensive review on cutting tool life prediction has not been reported in the literature till now. The future suggestions provided in this review are expected to provide avenues to solve the unexplored challenges in this field.