Seven-degree-of-freedom redundant manipulators with link offset have many advantages, including obvious geometric significance and suitability for configu-ration control. Their configuration is similar to that of the experimental module manipulator (EMM) in the Chinese Space Station Remote Manipulator System. However, finding the analytical solution of an EMM on the basis of arm angle parameterization is difficult. This study proposes a high-precision, semi-analytical inverse method for EMMs. Firstly, the analytical inverse kinematic solution is established based on joint angle parameterization. Secondly, the analytical inverse kinematic solution for a non-offset spherical–roll–spherical (SRS) redundant manipulator is derived based on arm angle parameterization. The approximate solution of the EMM is calculated in accordance with the relationship between the joint angles of the EMM and the SRS manipulator. Thirdly, the error is corrected using a numerical method through the analytical inverse solution based on joint angle parameterization. After selecting the stride and termination condition, the precise inverse solution is computed for the EMM based on arm angle parameterization. Lastly, case solutions confirm that this method has high precision, and the arm angle parameterization method is superior to the joint angle parameterization method in terms of parameter selection.
A redundantly actuated parallel manipulator (RAPM) with mixed translational and rotational degrees of freedom (DOFs) is challenged for its dimensionally homogeneous Jacobian modeling and optimal design of architecture. In this paper, a means to achieve redundant actuation by adding kinematic constraints is introduced, which reduces the DOFs of the end-effector (EE). A generic dimensionally homogeneous Jacobian is developed for this type of RAPMs, which maps the generalized velocities of three points on the EE to the joint velocities. A new optimization algorithm derived from this dimensionally homogeneous Jacobian is proposed for the optimal design of this type of RAPMs. As an example, this paper presents a spatial RAPM involving linkages and cam mechanisms. This RAPM has 4 DOFs and 6 translational actuations. The linkage lengths and the position of the universal joints of the RAPM are optimized based on the dimensionally homogeneous Jacobian.
Although the torso plays an important role in the movement coordination and versatile locomotion of mammals, the structural design and neuromechanical control of a bionic torso have not been fully addressed. In this paper, a parallel mechanism is designed as a bionic torso to improve the agility, coordination, and diversity of robot locomotion. The mechanism consists of 6-degree of freedom actuated parallel joints and can perfectly simulate the bending and stretching of an animal’s torso during walking and running. The overall spatial motion performance of the parallel mechanism is improved by optimizing the structural parameters. Based on this structure, the rhythmic motion of the parallel mechanism is obtained by supporting state analysis. The neural control of the parallel mechanism is realized by constructing a neuromechanical network, which merges the rhythmic signals of the legs and generates the locomotion of the bionic parallel mechanism for different motion patterns. Experimental results show that the complete integrated system can be controlled in real time to achieve proper limb–torso coordination. This coordination enables several different motions with effectiveness and good performance.
This study presents an improved compound control algorithm that substantially enhances the anti-disturbance performance of a gear-drive gyro-stabilized platform with a floating gear tension device. The tension device can provide a self-adjustable preload to eliminate the gap in the meshing process. However, the weaker gear support stiffness and more complex meshing friction are also induced by the tension device, which deteriorates the control accuracy and the ability to keep the aim point of the optical sensors isolated from the platform motion. The modeling and compensation of the induced complex nonlinearities are technically challenging, especially when base motion exists. The aim of this research is to cope with the unmeasured disturbances as well as the uncertainties caused by the base lateral motion. First, the structural properties of the gear transmission and the friction-generating mechanism are analyzed, which classify the disturbances into two categories: Time-invariant and time-varying parts. Then, a proportional-integral controller is designed to eliminate the steady-state error caused by the time-invariant disturbance. A proportional multiple-integral-based state augmented Kalman filter is proposed to estimate and compensate for the time-varying disturbance that can be approximated as a polynomial function. The effectiveness of the proposed compound algorithm is demonstrated by comparative experiments on a gear-drive pointing system with a floating gear tension device, which shows a maximum 76% improvement in stabilization precision.
Safe and effective autonomous navigation in dynamic environments is challenging for four-wheel independently driven steered mobile robots (FWIDSMRs) due to the flexible allocation of multiple maneuver modes. To address this problem, this study proposes a novel multiple mode-based navigation system, which can achieve efficient motion planning and accurate tracking control. To reduce the calculation burden and obtain a comprehensive optimized global path, a kinodynamic interior–exterior cell exploration planning method, which leverages the hybrid space of available modes with an incorporated exploration guiding algorithm, is designed. By utilizing the sampled subgoals and the constructed global path, local planning is then performed to avoid unexpected obstacles and potential collisions. With the desired profile curvature and preselected mode, a fuzzy adaptive receding horizon control is proposed such that the online updating of the predictive horizon is realized to enhance the trajectory-following precision. The tracking controller design is achieved using the quadratic programming (QP) technique, and the primal–dual neural network optimization technique is used to solve the QP problem. Experimental results on a real-time FWIDSMR validate that the proposed method shows superior features over some existing methods in terms of efficiency and accuracy.
In industrial manufacturing, the deployment of dual-arm robots in assembly tasks has become a trend. However, making the dual-arm robots more intelligent in such applications is still an open, challenging issue. This paper proposes a novel framework that combines task-oriented motion planning with visual perception to facilitate robot deployment from perception to execution and finish assembly problems by using dual-arm robots. In this framework, visual perception is first employed to track the effects of the robot behaviors and observe states of the workpieces, where the performance of tasks can be abstracted as a high-level state for intelligent reasoning. The assembly task and manipulation sequences can be obtained by analyzing and reasoning the state transition trajectory of the environment as well as the workpieces. Next, the corresponding assembly manipulation can be generated and parameterized according to the differences between adjacent states by combining with the prebuilt knowledge of the scenarios. Experiments are set up with a dual-arm robotic system (ABB YuMi and an RGB-D camera) to validate the proposed framework. Experimental results demonstrate the effectiveness of the proposed framework and the promising value of its practical application.
Remanufacturing, as one of the optimal disposals of end-of-life products, can bring tremendous economic and ecological benefits. Remanufacturing process planning is facing an immense challenge due to uncertainties and fuzziness of recoverable products in damage conditions and remanufacturing quality requirements. Although researchers have studied the influence of uncertainties on remanufacturing process planning, very few of them comprehensively studied the interactions among damage conditions and quality requirements that involve uncertain, fuzzy information. Hence, this challenge in the context of uncertain, fuzzy information is undertaken in this paper, and a method for remanufacturing process planning is presented to maximize remanufacturing efficiency and minimize cost. In particular, the characteristics of uncertainties and fuzziness involved in the remanufacturing processes are explicitly analyzed. An optimization model is then developed to minimize remanufacturing time and cost. The solution is provided through an improved Takagi–Sugeno fuzzy neural network (T-S FNN) method. The effectiveness of the proposed approach is exemplified and elucidated by a case study. Results show that the training speed and accuracy of the improved T-S FNN method are 23.5% and 82.5% higher on average than those of the original method, respectively.
Taping is often used to protect patterned wafers and reduce fragmentation during backgrinding of silicon wafers. Grinding experiments using coarse and fine resin-bond diamond grinding wheels were performed on silicon wafers with tapes of different thicknesses to investigate the effects of taping on peak-to-valley (PV), surface roughness, and subsurface damage of silicon wafers after grinding. Results showed that taping in backgrinding could provide effective protection for ground wafers from breakage. However, the PV value, surface roughness, and subsurface damage of silicon wafers with taping deteriorated compared with those without taping although the deterioration extents were very limited. The PV value of silicon wafers with taping decreased with increasing mesh size of the grinding wheel and the final thickness. The surface roughness and subsurface damage of silicon wafers with taping decreased with increasing mesh size of grinding wheel but was not affected by removal thickness. We hope the experimental finding could help fully understand the role of taping in backgrinding.
The interfacial wear between silicon and amorphous silica in water environment is critical in numerous applications. However, the understanding regarding the micro dynamic process is still unclear due to the limitations of apparatus. Herein, reactive force field simulations are utilized to study the interfacial process between silicon and amorphous silica in water environment, exploring the removal and damage mechanism caused by pressure, velocity, and humidity. Moreover, the reasons for high removal rate under high pressure and high velocity are elucidated from an atomic perspective. Simulation results show that the substrate is highly passivated under high humidity, and the passivation layer could alleviate the contact between the abrasive and the substrate, thus reducing the damage and wear. In addition to more Si-O-Si bridge bonds formed between the abrasive and the substrate, new removal pathways such as multibridge bonds and chain removal appear under high pressure, which cause higher removal rate and severer damage. At a higher velocity, the abrasive can induce extended tribochemical reactions and form more interfacial Si-O-Si bridge bonds, hence increasing removal rate. These results reveal the internal cause of the discrepancy in damage and removal rate under different conditions from an atomic level.
3D metal printing process has attracted increasing attention in recent years due to advantages, such as flexibility and rapid prototyping. This study aims to investigate the orientation effect of electropolishing characteristics on different surfaces of 316L stainless steel fabricated by laser powder bed fusion (L-PBF), considering that the rough surface of 3D printed parts is a key factor limiting its applications in the industry. The electropolishing characteristics on the different surfaces corresponding to the building orientation in selective laser melting are studied. Experimental results show that electrolyte temperature has critical importance on the electropolishing, especially for the vertical direction to the layering plane. The finish of electropolished surfaces is affected by the defects generated during L-PBF process. Thus, the electropolished vertical surface has higher surface roughness Sa than the horizontal surface. The X-ray photoelectron spectroscopy spectra show that the electropolished horizontal surface has higher Cr/Fe element ratio than the vertical surface. The electropolished horizontal surface presents higher corrosion resistance than the vertical surface by measuring the anodic polarization curves and fitting the equivalent circuit of experimental electrochemical impedance spectroscopy.
This paper proposes a novel method for the continuum topology optimization of transient vibration problem with maximum dynamic response constraint. An aggregated index in the form of an integral function is presented to cope with the maximum response constraint in the time domain. The density filter solid isotropic material with penalization method combined with threshold projection is developed. The sensitivities of the proposed index with respect to design variables are conducted. To reduce computational cost, the second-order Arnoldi reduction (SOAR) scheme is employed in transient analysis. Influences of aggregate parameter, duration of loading period, interval time, and number of basis vectors in the SOAR scheme on the final designs are discussed through typical examples while unambiguous configuration can be achieved. Through comparison with the corresponding static response from the final designs, the optimized results clearly demonstrate that the transient effects cannot be ignored in structural topology optimization.
This paper presents a MATLAB implementation of the material-field series-expansion (MFSE) topo-logy optimization method. The MFSE method uses a bounded material field with specified spatial correlation to represent the structural topology. With the series-expansion method for bounded fields, this material field is described with the characteristic base functions and the corresponding coefficients. Compared with the conventional density-based method, the MFSE method decouples the topological description and the finite element discretization, and greatly reduces the number of design variables after dimensionality reduction. Other features of this method include inherent control on structural topological complexity, crisp structural boundary description, mesh independence, and being free from the checkerboard pattern. With the focus on the implementation of the MFSE method, the present MATLAB code uses the maximum stiffness optimization problems solved with a gradient-based optimizer as examples. The MATLAB code consists of three parts, namely, the main program and two subroutines (one for aggregating the optimization constraints and the other about the method of moving asymptotes optimizer). The implementation of the code and its extensions to topology optimization problems with multiple load cases and passive elements are discussed in detail. The code is intended for researchers who are interested in this method and want to get started with it quickly. It can also be used as a basis for handling complex engineering optimization problems by combining the MFSE topology optimization method with non-gradient optimization algorithms without sensitivity information because only a few design variables are required to describe relatively complex structural topology and smooth structural boundaries using the MFSE method.
Metamodels have been widely used as an alternative for expensive physical experiments or complex, time-consuming computational simulations to provide a fast but accurate analysis. However, challenge remains in the prior determination of the most suitable metamodel for a particular case because of the lack of information about the actual behavior of a system. In addition, existing studies on metamodels have largely restricted on solving deterministic problems (e.g., data from finite element models), whereas some real-life engineering problems (e.g., data from physical experiment) are stochastic problems with noisy data. In this work, a robust ensemble of metamodels (EMs) is proposed by combining three regression stand-alone metamodels in a weighted sum form. The weight factor is adaptively determined according to the hybrid error metric, which combines global and local error measures to improve the accuracy of the EMs. Furthermore, three typical individual metamodels that can filter noise are selected to construct the EMs to extend their application in practical engineering problems. Three well-known benchmark problems with different levels of noise and three engineering problems are used to verify the effectiveness of the proposed EMs. Results show that the proposed EMs have higher accuracy and robustness than the individual metamodels and other typical EMs in major cases.
The general discrete scheme of time-varying Reynolds equation loses the information of the previous step, which makes it unreasonable. A discretization formula of the Reynolds equation, which is based on the Crank–Nicolson method, is proposed considering the physical message of the previous step. Gauss–Seidel relaxation and distribution relaxation are adopted for the linear operators of pressure during the numerical solution procedure. In addition to the convergent criteria of pressure distribution and load, an estimation framework is developed to investigate the relative error of the most important term in the Reynolds equation. Smooth surface with full contacts and mixed elastohydrodynamic lubrication is tested for validation. The asperity contact and sinusoidal wavy surface are examined by the proposed discrete scheme. Results show the precipitous decline in the boundary of the contact area. The relative error suggests that the pressure distribution is reliable and reflects the accuracy and effectiveness of the developed method.
