Current Status,Challenges,and Prospects for New Types of Aerial Robots

New types of aerial robots(NTARs)have found extensive applications in the military,civilian contexts,scientific research,disaster management,and various other domains.Compared with traditional aerial robots,NTARs exhibit a broader range of morphological diversity,locomotion capabilities,and enhanced operational capacities.Therefore,this study defines aerial robots with the four characteristics of morphability,biomimicry,multi-modal locomotion,and manipulator attachment as NTARs.Subsequently,this paper discusses the latest research progress in the materials and manufacturing technology,actuation technology,and perception and control technology of NTARs.Thereafter,the research status of NTAR systems is summarized,focusing on the frontier development and application cases of flapping-wing microair vehicles,perching aerial robots,amphibious robots,and operational aerial robots.Finally,the main challenges presented by NTARs in terms of energy,materials,and perception are analyzed,and the future development trends of NTARs are summarized in terms of size and endurance,mechatronics,and complex scenarios,providing a reference direction for the follow-up exploration of NTARs.

Mesh representation matters:investigating the influence of different mesh features on perceptual and spatial fidelity of deep 3D morphable models

Background Deep 3D morphable models(deep 3DMMs)play an essential role in computer vision.They are used in facial synthesis,compression,reconstruction and animation,avatar creation,virtual try-on,facial recognition systems and medical imaging.These applications require high spatial and perceptual quality of synthesised meshes.Despite their significance,these models have not been compared with different mesh representations and evaluated jointly with point-wise distance and perceptual metrics.Methods We compare the influence of different mesh representation features to various deep 3DMMs on spatial and perceptual fidelity of the reconstructed meshes.This paper proves the hypothesis that building deep 3DMMs from meshes represented with global representations leads to lower spatial reconstruction error measured with L_(1) and L_(2) norm metrics and underperforms on perceptual metrics.In contrast,using differential mesh representations which describe differential surface properties yields lower perceptual FMPD and DAME and higher spatial fidelity error.The influence of mesh feature normalisation and standardisation is also compared and analysed from perceptual and spatial fidelity perspectives.Results The results presented in this paper provide guidance in selecting mesh representations to build deep 3DMMs accordingly to spatial and perceptual quality objectives and propose combinations of mesh representations and deep 3DMMs which improve either perceptual or spatial fidelity of existing methods.

Reliability-Based Topology Optimization of Fail-Safe Structures Using Moving Morphable Bars

This paper proposes an effective reliability design optimizationmethod for fail-safe topology optimization(FSTO)considering uncertainty based on the moving morphable bars method to establish the ideal balance between cost and robustness,reliability and structural safety.To this end,a performancemeasure approach(PMA)-based doubleloop optimization algorithmis developed tominimize the relative volume percentage while achieving the reliability criterion.To ensure the compliance value of the worst failure case can better approximate the quantified design requirement,a p-norm constraint approach with correction parameter is introduced.Finally,the significance of accounting for uncertainty in the fail-safe design is illustrated by contrasting the findings of the proposed reliabilitybased topology optimization(RBTO)method with those of the deterministic design method in three typical examples.Monte Carlo simulation shows that the relative error of the reliability index of the optimized structure does not exceed 3%.

Explicit Isogeometric Topology Optimization Method with Suitably Graded Truncated Hierarchical B-Spline

This work puts forward an explicit isogeometric topology optimization(ITO)method using moving morphable components(MMC),which takes the suitably graded truncated hierarchical B-Spline based isogeometric analysis as the solver of physical unknown(SGTHB-ITO-MMC).By applying properly basis graded constraints to the hierarchical mesh of truncated hierarchical B-splines(THB),the convergence and robustness of the SGTHB-ITOMMC are simultaneously improved and the tiny holes occurred in optimized structure are eliminated,due to the improved accuracy around the explicit structural boundaries.Moreover,an efficient computational method is developed for the topological description functions(TDF)ofMMC under the admissible hierarchicalmesh,which consists of reducing the dimensionality strategy for design space and the locally computing strategy for hierarchical mesh.We apply the above SGTHB-ITO-MMC with improved efficiency to a series of 2D and 3Dcompliance design problems.The numerical results show that the proposed SGTHB-ITO-MMC method outperforms the traditional THB-ITO-MMCmethod in terms of convergence rate and efficiency.Therefore,the proposed SGTHB-ITO-MMC is an effective way of solving topology optimization(TO)problems.

Explicit Topology Optimization Design of Stiffened Plate Structures Based on the Moving Morphable Component(MMC)Method

This paper proposes an explicit method for topology optimization of stiffened plate structures.The present work is devoted to simultaneously optimizing stiffeners’shape,size and layout by seeking the optimal geometry parameters of a series of moving morphable components(MMC).The stiffeners with straight skeletons and the stiffeners with curved skeletons are considered to enhance the modeling and optimization capability of the current approach.All the stiffeners are represented under the Lagrangian-description framework in a fully explicit way,and the adaptive ground structure method,as well as dynamically updated plate/shell elements,is used to obtain optimized designs with more accurate analysis results.Compared with existing works,the proposed approach provides an explicit description of the structure.Thus,a stiffened plate structure with clear stiffener distribution and smooth geometric boundary can be obtained.Several numerical examples provided,including straight and curved stiffeners,hierarchical stiffeners,and a stiffened plate with a cutout,validate the effectiveness and applicability of the proposed approach.

Multiresolution Isogeometric Topology Optimisation Using Moving Morphable Voids

A general and new explicit isogeometric topology optimisation approach with moving morphable voids(MMV)is proposed.In this approach,a novel multiresolution scheme with two distinct discretisation levels is developed to obtain high-resolution designs with a relatively low computational cost.Ersatz material model based on Greville abscissae collocation scheme is utilised to represent both the Young’s modulus of the material and the density field.Two benchmark examples are tested to illustrate the effectiveness of the proposed method.Numerical results show that high-resolution designs can be obtained with relatively low computational cost,and the optimisation can be significantly improved without introducing additional DOFs.

Recent progress of morphable 3D mesostructures in advanced materials

Soft robots complement the existing efforts of miniaturizing conventional,rigid robots,and have the potential to revolutionize areas such as military equipment and biomedical devices.This type of system can accomplish tasks in complex and time-varying environments through geometric reconfiguration induced by diverse external stimuli,such as heat,solvent,light,electric field,magnetic field,and mechanical field.Approaches to achieve reconfigurable mesostructures are essential to the design and fabrication of soft robots.Existing studies mainly focus on four key aspects:reconfiguration mechanisms,fabrication schemes,deformation control principles,and practical applications.This review presents a detailed survey of methodologies for morphable mesostructures triggered by a wide range of stimuli,with a number of impressive examples,demonstrating high degrees of deformation complexities and varied multi-functionalities.The latest progress based on the development of new materials and unique design concepts is highlighted.An outlook on the remaining challenges and open opportunities is provided.

Thermoelastic Structural Topology Optimization Based on Moving Morphable Components Framework

This study investigates structural topology optimization of thermoelastic structures considering two kinds of objectives ofminimumstructural compliance and elastic strain energy with a specified available volume constraint.To explicitly express the configuration evolution in the structural topology optimization under combination of mechanical and thermal load conditions,the moving morphable components(MMC)framework is adopted.Based on the characteristics of the MMC framework,the number of design variables can be reduced substantially.Corresponding optimization formulation in the MMC topology optimization framework and numerical solution procedures are developed for several numerical examples.Different optimization results are obtained with structural compliance and elastic strain energy as objectives,respectively,for thermoelastic problems.The effectiveness of the proposed optimization formulation is validated by the numerical examples.It is revealed that for the optimization design of the thermoelastic structural strength,the objective function with the minimum structural strain energy can achieve a better performance than that from structural compliance design.

An Effective Surface Modeling Method for Car Styling from a Side-View Image

We introduce an almost-automatic technique for generating 3D car styling surface models based on a single side-view image. Our approach combines the prior knowledge of car styling and deformable curve network model to obtain an automatic modeling process. Firstly, we define the consistent parameterized curve template for 2D and 3D case respectivelyby analyzingthe characteristic lines for car styling. Then, a semi-automatic extraction from a side-view car image is adopted. Thirdly, statistic morphable model of 3D curve network isused to get the initial solution with sparse point constraints.Withonly afew post-processing operations, the optimized curve network models for creating surfaces are obtained. Finally, the styling surfaces are automatically generated using template-based parametric surface modeling method. More than 50 3D curve network models are constructed as the morphable database. We show that this intelligent modeling toolsimplifiesthe exhausted modeling task, and also demonstratemeaningful results of our approach.

Measuring 3D face deformations from RGB images of expression rehabilitation exercises

Background The accurate(quantitative)analysis of 3D face deformation is a problem of increasing interest in many applications.In particular,defining a 3D model of the face deformation into a 2D target image to capture local and asymmetric deformations remains a challenge in existing literature.A measure of such local deformations may be a relevant index for monitoring the rehabilitation exercises of patients suffering from Par-kinson’s or Alzheimer’s disease or those recovering from a stroke.Methods In this paper,a complete framework that allows the construction of a 3D morphable shape model(3DMM)of the face is presented for fitting to a target RGB image.The model has the specific characteristic of being based on localized components of deformation.The fitting transformation is performed from 3D to 2D and guided by the correspondence between landmarks detected in the target image and those manually annotated on the average 3DMM.The fitting also has the distinction of being performed in two steps to disentangle face deformations related to the identity of the target subject from those induced by facial actions.Results The method was experimentally validated using the MICC-3D dataset,which includes 11 subjects.Each subject was imaged in one neutral pose and while performing 18 facial actions that deform the face in localized and asymmetric ways.For each acquisition,3DMM was fit to an RGB frame whereby,from the apex facial action and the neutral frame,the extent of the deformation was computed.The results indicate that the proposed approach can accurately capture face deformation,even localized and asymmetric deformations.Conclusion The proposed framework demonstrated that it is possible to measure deformations of a reconstructed 3D face model to monitor facial actions performed in response to a set of targets.Interestingly,these results were obtained using only RGB targets,without the need for 3D scans captured with costly devices.This paves the way for the use of the proposed tool in remote medical rehabilitation monitoring.

One-shot Face Reenactment with Dense Correspondence Estimation

One-shot face reenactment is a challenging task due to the identity mismatch between source and driving faces.Most existing methods fail to completely eliminate the interference of driving subjects’identity information,which may lead to face shape distortion and undermine the realism of reenactment results.To solve this problem,in this paper,we propose using a 3D morphable model(3DMM)for explicit facial semantic decomposition and identity disentanglement.Instead of using 3D coefficients alone for reenactment control,we take advantage of the generative ability of 3DMM to render textured face proxies.These proxies contain abundant yet compact geometric and semantic information of human faces,which enables us to compute the face motion field between source and driving images by estimating the dense correspondence.In this way,we can approximate reenactment results by warping source images according to the motion field,and a generative adversarial network(GAN)is adopted to further improve the visual quality of warping results.Extensive experiments on various datasets demonstrate the advantages of the proposed method over existing state-of-the-art benchmarks in both identity preservation and reenactment fulfillment.

Sphere Face Model: A 3D morphable model with hypersphere manifold latent space using joint 2D/3D training

3D morphable models(3DMMs)are generative models for face shape and appearance.Recent works impose face recognition constraints on 3DMM shape parameters so that the face shapes of the same person remain consistent.However,the shape parameters of traditional 3DMMs satisfy the multivariate Gaussian distribution.In contrast,the identity embeddings meet the hypersphere distribution,and this conflict makes it challenging for face reconstruction models to preserve the faithfulness and the shape consistency simultaneously.In other words,recognition loss and reconstruction loss can not decrease jointly due to their conflict distribution.To address this issue,we propose the Sphere Face Model(SFM),a novel 3DMM for monocular face reconstruction,preserving both shape fidelity and identity consistency.The core of our SFM is the basis matrix which can be used to reconstruct 3D face shapes,and the basic matrix is learned by adopting a twostage training approach where 3D and 2D training data are used in the first and second stages,respectively.We design a novel loss to resolve the distribution mismatch,enforcing that the shape parameters have the hyperspherical distribution.Our model accepts 2D and 3D data for constructing the sphere face models.Extensive experiments show that SFM has high representation ability and clustering performance in its shape parameter space.Moreover,it produces highfidelity face shapes consistently in challenging conditions in monocular face reconstruction.The code will be released at https://github.com/a686432/SIR.

Topology Optimization of Acoustic-Mechanical Structures for Enhancing Sound Quality

Sound quality is one of the essential criteria for measuring the acoustic performance of acoustic devices.In contrast to the optimization of sound characteristics,both the quantitative description of sound quality and the numerical instability that may occur during optimization need to be investigated.In the present work,an explicit topology optimization approach is proposed to enhance the sound quality of acoustic-mechanical structures,where the sound quality is described,resorting to frequency response within a specified frequency band.To this end,the moving morphable component(MMC)-based approach is adopted to achieve the explicit topology design,and the mixed finite element method is introduced to evaluate the sound quality.With the use of the explicit description of MMC,the acoustic-structure boundary can be captured accurately,which is important for acoustic response analysis.Moreover,a regularization topology optimization formulation is also developed to avoid the numerical issues produced in some special frequency bands.Numerical examples demonstrate the effectiveness of the proposed approach in improving sound quality performance.

Self-consistent Clustering Analysis-Based Moving Morphable Component(SMMC)Method for Multiscale Topology Optimization

Current multiscale topology optimization restricts the solution space by enforcing the use of a few repetitive microstructures that are predetermined,and thus lack the ability for structural concerns like buckling strength,robustness,and multi-functionality.Therefore,in this paper,a new multiscale concurrent topology optimization design,referred to as the self-consistent analysis-based moving morphable component(SMMC)method,is proposed.Compared with the conventional moving morphable component method,the proposed method seeks to optimize both material and structure simultaneously by explicitly designing both macrostructure and representative volume element(RVE)-level microstructures.Numerical examples with transducer design requirements are provided to demonstrate the superiority of the SMMC method in comparison to traditional methods.The proposed method has broad impact in areas of integrated industrial manufacturing design:to solve for the optimized macro and microstructures under the objective function and constraints,to calculate the structural response efficiently using a reduced-order model:self-consistent analysis,and to link the SMMC method to manufacturing(industrial manufacturing or additive manufacturing)based on the design requirements and application areas.

An Optimization Approach for Stiffener Layout of Composite Stiffened Panels Based on Moving Morphable Components(MMCs)

An explicit topology optimization method for the stiffener layout of composite stiffened panels is proposed based on moving morphable components(MMCs).The skin and stiffeners are considered as panels with different bending stiffnesses,with the use of equivalent stiffness method.Then the location and geometric properties of composite stiffeners are determined by several MMCs to perform topology optimization,which can greatly simplify the finite element model.With the objective of maximizing structural stiffness,several typical cases with various loading and boundary conditions are selected as numerical examples to demonstrate the proposed method.The numerical examples illustrate that the proposed method can provide clear stiffener layout and explicit geometry information,which is not limited within the framework of parameter and size optimization.The mechanical properties of composite stiffened panels can be fully enhanced.

Isogeometric topology optimization based on energy penalization for symmetric structure

We present an energy penalization method for isogeometric topology optimization using moving morphable components(ITO–MMC),propose an ITO–MMC with an additional bilateral or periodic symmetric constraint for symmetric structures,and then extend the proposed energy penalization method to an ITO–MMC with a symmetric constraint.The energy penalization method can solve the problems of numerical instability and convergence for the ITO–MMC and the ITO–MMC subjected to the structural symmetric constraint with asymmetric loads.Topology optimization problems of asymmetric,bilateral symmetric,and periodic symmetric structures are discussed to validate the effectiveness of the proposed energy penalization approach.Compared with the conventional ITO–MMC,the energy penalization method for the ITO–MMC can improve the convergence rate from 18.6%to 44.5%for the optimization of the asymmetric structure.For the ITO–MMC under a bilateral symmetric constraint,the proposed method can reduce the objective value by 5.6%and obtain a final optimized topology that has a clear boundary with decreased iterations.For the ITO–MMC under a periodic symmetric constraint,the proposed energy penalization method can dramatically reduce the number of iterations and obtain a speedup of more than 2.

Topology optimization of plate structures using plate element-based moving morphable component(MMC)approach

A topology optimization approach for designing the layout of plate structures is proposed in this article.In this approach,structural mechanical behavior is analyzed under the framework of Kirchhoff plate theory,and structural topology is described explicitly by a set of moving morphable components.Compared to the existing treatments where structural topology is generally described in an implicit manner,the adopted explicit geometry/layout description has demonstrated its advantages on several aspects.Firstly,the number of design variables is reduced substantially.Secondly,the obtained optimized designs are pure black-and-white and contain no gray regions.Besides,numerical experiments show that the use of Kirchhoff plate element helps save 95-99%computational time,compared with traditional treatments where solid elements are used for finite element analysis.Moreover the accuracy of the proposed method is also validated through a comparison with the corresponding theoretical solutions.Several numerical examples are also provided to demonstrate the effectiveness of the proposed approach.

Explicit Topology Optimization with Moving Morphable Component(MMC)Introduction Mechanism

In this article,an explicit topology optimization approach with components-growing ability is proposed under the moving morphable component(MMC)framework.In this approach,the shape and topology layout of structures are explicitly optimized by growth evolution of moving morphable components.To this end,a competition criterion is developed to optimize the structural layout from two options:adding several new components or changing the current layout.In addition,some numerical technqiues are also developed to preserve the stability of the iterative process.The present topology optimization approach allows rational generation of new components and does not require a specific distribution of components in the initial design,which is compulsory for the conventional MMC method.Three numerical examples are provided to illustrate the effectiveness of the proposed method.The optimization results indicate that the proposed method does have the potential to improve the existing MMC-based explicit topology optimization framework by eliminating the initial design dependency of optimal solutions.

Hybrid algorithms for handling the numerical noise in topology optimization

This paper presents new hybrid methods for the identification of optimal topologies by combining the teaching-learning based optimization(TLBO)and the method of moving asymptotes(MMA).The topology optimization problem is parameterizing with a low dimensional explicit method called moving morphable components(MMC),to make the use of evolutionary algorithms more efficient.Gradient-based solvers have good performance in solving large-scale topology optimization problems.However,in unconventional cases same as crashworthiness design in which there is numerical noise in the gradient information,the uses of these algorithms are unsuitable.The standard evolutionary algorithms can solve such problems since they don’t need gradient information.However,they have a high computational cost.This paper is based upon the idea of combining metaheuristics with mathematical programming to handle the probable noises and have faster convergence speed.Due to the ease of computations,the compliance minimization problem is considered as the case study and the artificial noise is added in gradient information.

Connected morphable components-based multiscale topology optimization

The advances of manufacturing techniques,such as additive manufacturing,have provided unprece-dented opportunities for producing multiscale structures with intricate latticed/cellular material microstructures to meet the increasing demands for parts with customized functionalities.However,there are still difficulties for the state-of-the-art multiscale topology optimization(TO)methods to achieve manufacturable multiscale designs with cellular materials,partially due to the disconnectivity issue when tiling material microstructures.This paper attempts to address the disconnectivity issue by extending component-based TO methodology to multiscale structural design.An effective linkage scheme to guarantee smooth transitions between neighboring material microstructures(unit cells)is devised and investigated.Associated with the advantages of components-based TO,the number of design variables is greatly reduced in multiscale TO design.Homogenization is employed to calculate the effective material properties of the porous materials and to correlate the macro/structural scale with the micro/material scale.Sensitivities of the objective function with respect to the geometrical parameters of each component in each material microstructure have been derived using the adjoint method.Numerical examples demonstrate that multiscale structures with well-connected material microstructures or graded/layered material microstructures are realized.