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Abstract Deterministic Sampling Methods for Spheres and SO(3)

Abstract Deterministic Sampling Methods for Spheres and SO(3)
Abstract Deterministic Sampling Methods for Spheres and SO(3)

Deterministic Sampling Methods for Spheres and SO(3) Anna Yershova Steven https://www.doczj.com/doc/6d529820.html,Valle

Dept.of Computer Science

University of Illinois

Urbana,IL61801USA

{yershova,lavalle}@https://www.doczj.com/doc/6d529820.html,

Abstract

This paper addresses the problem of generating uni-form deterministic samples over the spheres and the three-dimensional rotation group,SO(3).The target applications include motion planning,optimization, and veri?cation problems in robotics and in related areas,such as graphics,control theory and computa-tional biology.We introduce an in?nite sequence of samples that is shown to achieve:1)low-dispersion, which aids in the development of resolution complete algorithms,2)lattice structure,which allows easy neighbor identi?cation that is comparable to what is obtained for a grid in R d,and3)incremental qual-ity,which is similar to that obtained by random sam-pling.The sequence is demonstrated in a sampling-based motion planning algorithm.

1Introduction

Many important algorithms developed in robotics and related areas require careful sampling over spheres.In recent years,the paradigm of sampling-based motion planning has led to algorithms that can solve many challenging problems by combining colli-sion detection,search algorithms,and sampling strate-gies over the con?guration space.General sampling over spheres arises in many forms of planning and op-timization in which some number of directions are lo-cally explored.For example,some potential?eld ap-proaches[3,10]involve sampling local directions to obtain an approximate gradient descent.The exact expression of the gradient may be too costly or even unavailable.One important special case of sampling over spheres is sampling over the3D rotation group, SO(3),which involves sampling over half of the three-sphere,S3.One of the main motivations for this

paper

Figure1:Distribution of points on the sphere S2gen-erated by a grid(Sukharev[20])on each spherical face.

is the problem of motion planning for a rigid body in R3.

We are particularly interested in the development of deterministic sampling methods.Although most existing motion planning methods currently use ran-dom sampling,they are limited to probabilistic forms of completeness.With deterministic sampling,reso-lution completeness guarantees are possible.This is particularly valuable in the area of system veri?ca-tion,in which one must guarantee that a system be-haves correctly under all possible trajectories.The intractability of most of these problems leads natu-rally to sampling based approaches.While it may be valuable to verify a system down to some level of res-olution,random sampling might leave doubts about whether the space was adequately covered.In some cases,deterministic sampling has even led to practical performance improvements in comparison to random sampling[13,14,15].The techniques presented in the present paper build on recent work to develop uniform, deterministic sampling techniques for motion planning [6,12,13].

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The particular problem of sampling over spheres presents many unique challenges.The vast majority of sampling literature considers placing points in a unit d-dimensional cube,[0,1]d?R d(see[12,15]).This might correctly capture some con?guration spaces that arise in robotics;however,the majority of appli-cations involve other topological spaces,such as R P3, which arises from rigid body rotations,or toroidal manifolds,which arise from a series of revolute joints of a manipulator.In these cases,special sampling techniques should be developed because quality mea-sures for sets of samples depend on the topology.For example,the maximum distance that a con?guration could be from its nearest sample depends on the met-ric,which is induced partly by the topology.

In addition to topological issues,the way that a con?guration space is parameterized is of critical im-portance to de?ning notions of uniformity.A col-lection of samples that are uniform with respect to one parameterization of the con?guration space might seem extremely biased using another parameteriza-tion.It might seem that there is no way to avoid this frustrating issue,but fortunately for the case of SO(3),there is an intrinsic notion of uniformity that is given by the Haar measure[7](this will be de?ned in Section2).Using this notion,the natural parameter-ization of SO(3)is the set of unit quaternions(with antipodal identi?cation),and our sampling methods will be developed to achieve rigorous notions of uni-formity in this case.

To maximize the potential for impact on motion planning and related areas,our goal has been to de-velop a sampling method that achieves1)uniformity, 2)lattice structure,and3)incremental quality.Uni-formity means good covering of the space is obtained without unwanted bias,clumping or gaps.This can be formulated in terms of optimizing discrepancy or dis-persion[14,15,4].The uniformity notion considered here is actually more“uniform”than what is obtained by random https://www.doczj.com/doc/6d529820.html,ttice structure means that for every sample,the location of nearby samples can eas-ily be determined as part of a regular pattern(as in neighbors on a grid,for example).Incremental quality means that if the sampling method is considered as an in?nite sequence,then the sequence may be truncated after any?nite number of samples and good coverage will be obtained.This is an important characteris-tic of pseudo-random number sequences,making them desirable for many past motion planning algorithms [1,5,9,19,22].We would like to obtain the same behavior,even though the sequence is deterministic, uniform,and has lattice structure.

2Quality Measures for the Distribu-tions of Points on Spheres

We consider generating samples over spheres and SO(3).Let S d represent a d-dimensional sphere,em-bedded in R d+1as

S d={x∈R d+1| x =1}.

The set of all rotations in R3is denoted as SO(3), which is de?ned as the set of all3×3orthonormal ma-trices.It will be helpful to sometimes represent SO(3) as the set,H,of unit quaternions,each of which is ex-pressed as h=a+bi+cj+dk,with the identi?cation h~?h[11].Note that it appears that H=S3, except that antipodal points on S3are identi?ed in the de?nition of H.This leads to a close relationship between sampling on sphere and sampling on SO(3).

Now that the spaces have been de?ned,the next task is to de?ne the quality of samples.Consider sphere S d over which the?nite set of points A is gen-erated.

De?nition2.1For a?nite point set A generated over the sphere S d the discrepancy of A with respect to a given family R of subsets of S d,called ranges,is de?ned by

D R(A)=sup

R∈R

|A∩R||A|?μ(R)

,

whereμdenotes the rotation invariant measure of the sphere S d in Euclidean space R d+1,and|·|applied to a?nite set denotes its cardinality.

In the case of SO(3)the measure de?ned on S3as above corresponds to the Haar measure de?ned over the set of all rotation matrices.

The range spaces that are usually considered on the sphere are the set of spherical caps,i.e.,intersections of the sphere with half spaces;or the set of spherical slices,i.e.,intersections of two half-spheres[4,16].

De?nition2.2The dispersion of a?nite set A is de-?ned by

d R(A)=sup

q∈S d

min

p∈A

ρ(q,p),

in whichρis a rotation invariant metric over S d.

Having these de?nitions of uniformity in mind,in what follows we propose a general approach to sam-pling on spheres and SO(3).As a particular example we show how to generate a low-dispersion and low-discrepancy sample set which has additional useful

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properties:it is incremental,has lattice structure,and it can be e?ciently generated.We show how these samples can be applied to the problems of motion planning.

3Exploiting the Regularity of Pla-tonic Solids

Our general approach to sampling is based on Pla-tonic solids.In R3,a Platonic solid or regular polyhe-dron,is a polyhedron for which every face is a copy of a regular polygon,?xed over all faces,and the degree of every vertex is?xed.Let(v,e,f)denote the numbers of vertices,edges,and faces of a regu-lar polyhedron.Although there are an in?nite num-ber of regular polygons,there are only?ve regular polyhedra:tetrahedron(4,6,4),cube(8,12,6),octahe-dron(6,12,8),icosahedron(12,30,20),and dodecahe-dron(20,30,12).The notion of regular polyhedron can be generalized to higher dimensions to obtain a regular polytope.In R4,it turns out that there are six regu-lar polytopes:simplex(5,10,10,5),cube(16,32,24,8), cross polytope(8,24,32,16),24cell(24,96,96,24),120 cell(600,1200,720,120),600cell(120,720,1200,600). The forth element in each sequence denotes the num-ber of3D cells(which are regular polyhedra).Finally, in R d for any d>4,there are only three regular poly-topes:simplex,cube,and cross polytope.

We?rst address the problem of generating a uni-formly distributed set of points over S d.Consider in-scribing any(d+1)-dimensional regular polytope in-side of S d,so that all of its n vertices lie in S d.The set of vertices are beautifully arranged around S d so that the points are evenly spaced.Furthermore,the edges of the polytope yield a regular lattice structure that is natural for building roadmaps in planning problems. For the case of sampling SO(3),we simply use a set of vertices that lie in one hemisphere(making sure that no antipodal pairs of points appear in the set). The edges can be obtained directly from the polytope by making the appropriate identi?cation of antipodal pairs.

Unfortunately,there are only a few combinations of n and d,for which these ideal samples may be con-structed for S d and SO(3).This might be suitable for some applications,such as picking a set of candidate directions from S d for gradient descent of a potential function;however,in general,we would like to a have a nice distribution of points for any value of n.

To the best of our knowledge,it is impossible to perfectly space n points around S d,for any n and for

d>1.One simple idea that increases the number

of samples is place one point in the center of each of the c d-cells of some regular polytope,and lift it to S d.If we take the union of these points with the set of v polytope vertices,a nice point set of size c+v may be obtained.If more points are placed;however, the problem becomes more complicated.Therefore, we are willing to tolerate some distortion in the dis-tribution of points.It still seems useful,however,to borrow some of the properties of the regular polytopes to generate good samples.The general idea pursued in this paper is to sample uniformly on the surface of the regular polytope,and then transform generated distribution on the surface of the sphere.We next describe this general method and discuss the induced distortion.

Consider a(d+1)-dimensional regular polytope in-scribed in the sphere S d.Suppose there exists a good method of sampling the surface of this polytope.The faces(d-dimensional cells)of the polytope,if projected outward to the surface of the sphere,form a tiling of the surface with the d-dimensional spherical polytopes.

Consider some particular face,F,and its correspond-ing spherical face,F .Each point inside F can be described by the barycentric coordinate systems in-duced by vertices of F after its triangulation.Now imagine that a distribution of points is generated in-side F.Each of the points in this distribution can be obtained through several steps of linear interpola-tion between the vertices of the barycentric coordinate systems.The distribution on F can be obtained then through similar steps of interpolating between the ver-tices of F ,except that the interpolation should be done on the surface of the sphere[17].This idea is similar to the one proposed in[2]for strati?ed sam-pling of spherical triangles.As an example,consider a cube inscribed in the sphere S2,and sample the sur-face of the cube by putting the Sukharev grid[12,20] on each square https://www.doczj.com/doc/6d529820.html,ing the proposed method we get a distribution of samples on S2as shown on Figure 1.

The distribution of points on the sphere S d ob-tained by this method will introduce distortion since spherical arcs corresponding to the intervals inside F with the same length may have di?erent lengths in F .

The amount of the distortion,and therefore bounds on the dispersion and discrepancy,can be obtained through the analysis of the maximal arc di?erences.

This idea can also be adapted to SO(3)(and in gen-eral to the projective space of any dimension).Take a four-dimensional regular polytope inscribed in S3and use only half of the faces to generate the distribution 3

on the surface.We pick the faces so that in the set of used faces,there must not exist a pair of antipo-dal points,one from each of two di?erent faces.This way the obtained samples will cover exactly half of the sphere,which forms SO(3)surface.

Next we show how to generate a layered Sukharev grid sequence on S d based on the inscribed cube and the bounds on the dispersion and the discrepancy of this sequence.

4A Sample Sequence Based on Cubes

In this section?rst we make an overview of the techniques existing for sampling unit cubes.Next we show a particular sequence adapted to the spheres us-ing the proposed general method and we analyze the uniformity properties of this sequence.

4.1Sampling in Cubes

The subject of uniform sampling inside unit cube [0,1]d has been studied extensively for decades(see [14,15]).Here are some brief concepts.

There are two main sampling families that are con-sidered in the literature:point sets and sequences.

For a point set,the number of points,n,that should be placed in the set is speci?ed in advance,and a set of n points is chosen so that the sampling criterion (dispersion or discrepancy)is optimized.The notion of ordering between the points is not de?ned for the point sets.As an example we could consider the point sets generated by classical grid and Sukharev grid[20] of resolution l in[0,1]d.Each of these sets contains l points per axis and l d points total.The di?erence be-tween them is in the way each of these grids places its points in each of the l d subregions of the cube.Clas-sical grid places a vertex in the origin of each region, whereas Sukharev grid places a vertex at the center of each region.It was proven that the Sukharev grid optimizes the l∞dispersion over all of the point sets of size l d[15,20].Classical grid has low dispersion but is not dispersion optimal.

For sequences the ordering of the points becomes important.Each next point in a sequence should be chosen so that the sampling criterion is optimized.Se-quences are particularly suitable for the motion plan-ning algorithms,where the number of points needed to solve a given problem is not known in advance.

When designing sequences that optimize dispersion, it is useful to consider multiresolution grid sequences [13].A multiresolution grid of resolution l is a grid with2l points per axis and2dl points total.From this

de?nition it follows that a grid of resolution l contains all of the points from resolution l?1.The natural way to make this grid incremental is to build it one resolution at a time.During construction of the points from the same resolution level,the recursive procedure at each step adds those points that maximally decrease the discrepancy of the sequence,which extends van der Corput’s one-dimensional sequence[21].

As an example,consider a square,[0,1]2,with four grid points inside.The best order of placing these points is:(0,0),(0.5,0.5),(0,0.5),(0.5,0).To add the next12points from resolution3,what point should be placed?rst,second,and third out of this sequence?The idea is that every four points should follow the same ordering of quadrants as the?rst four points(i.e.,the?rst point should fall into the left-bottom rectangle,the next into right-top,and so on).

Where exactly the point should be placed within the left-bottom rectangle should be decided by the same criterion that was used to place the?rst4points.In this case the next point is(0.25,0.25).

The resulting sequence has several important prop-erties:it is incremental,it has low dispersion at each resolution level,it has optimal discrepancy with re-spect to the set of canonical rectangles,it has lattice structure,and there are e?cient methods for gener-ating the sequence and performing nearest neighbor queries on it[13].This makes multiresolution grid sequences particularly useful for motion planning ap-plications.

We will be using a layered version of this sequence.

A layered Sukharev grid of resolution l is a point set

containing all the points of Sukharev grids of res-olutions1,2,4,...2l.It follows that this grid has n=

l i=0(2i)d=(2d(l+1)?1)/(2d?1)points total.

A layered Sukharev grid sequence builds one

Sukharev grid of resolution2i at a time,i=1,2,....

Points from each of these grids then are generated by the same procedure as for building multiresolution grid sequences.

In what follows we generalize layered Sukharev grid sequence to the sphere S d.We?rst show how the points should be generated in each of the spherical cubes,and then how all these points can be combined into one sequence on the sphere.

4.2Layered Sukharev Grid Sequence for

a Spherical Cube

Consider a face,F,of a(d+1)-cube inscribed in a sphere S d.F is a d-dimensional cube,which in each of its corners has d edges.If we project all of these

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edges onto the surface of the sphere they form arcs, which delineate a spherical d-cube,F .The lengths,α,of these arcs are equal for all edges of F.If we consider those equatorial angles that correspond to the edges coming from a common vertex of F,we can de?ne an angular coordinate system for the spherical face F .Indeed,the coordinates(x1,x2,...x n?1)with all possible values x i∈[0,α]specify all possible points of F .

The construction of the sequence,T,essentially fol-lows the construction of the layered Sukharev grid se-quence for the unit cube,except that instead of the Euclidean coordinate system we use the angular coor-dinate system de?ned above.

To analyze the dispersion and discrepancy of this sequence we need several de?nitions.De?ne the points of the Sukharev spherical grid of resolution2l as fol-lows:

P d l= i1α2l+12l+1,i2α2l+12l+1,...,i dα2l+12l+1 :

i∈Z,0≤i≤2l?1

Next we de?ne the set of spherical canonical rectan-gles,which is an extension to the canonical rectangles de?ned in[13].

De?nition4.1Given positive integers d and m,let Q d m be the following family of the d-dimensional spher-ical canonical rectangles:

Q d m= i1α2m,(i1+j1)α2m ×...× i dα2m,(i d+j d)α2m : i,j∈Z,0≤i≤2m?1,1≤j≤min(2m?i,2)

The following results can be stated about the dis-persion and discrepancy of T.

Proposition4.2The dispersion of the sequence T at the resolution level,l,is

dρ(T)≤

d

n(2?1)+1

Proof:The largest spherical cap which does not con-tain any of the points in T will be smaller than the spherical cap with the center at(α/2,α/2,...,α/2) and the spherical radiusπ/2l.Since2l= d n(2d?1)+1 /2we have that the dispersion is not bigger thanπ/2l=2π/ d d .

Proposition4.3The relationship between the dis-crepancy of the sequence T at the resolution level,l, taken over Q d l=l m=0Q d m and the discrepancy of the optimal over Q d l sequence,T o,is:

D Q d l(T)≤D Q d l(T o)+(V max?V min)

Proof:The optimal sequence,T o,may place the points in some di?erent order than T.The maximal change in discrepancy that may occur in T comparing to T o is the di?erence between the maximal,V max,and the minimal,V min,volumes of the spherical canonical rectangles.Therefore,D Q d l(T)≤D Q d l(T o)+(V max?V min)

Proposition4.4The sequence T has the following properties:

?The position of the i-th sample in the sequence T

can be generated in O(log i)time.

?For any i-th sample any of the2d nearest grid

neighbors from the same layer can be found in

O((log i)/d)time.

Proof:For the i-th sample it takes O(log2d i)= O((log i)/d)to?nd its resolution level l.Once l is found,the corresponding point in Sukharev grid of res-olution2l needs to be generated.It was proved in[13] that this takes O(log i).Therefore,the total running time for generating one point is O((log i)/d+log i)= O(log i).

The layer of the i-th sample is the Sukharev grid of resolution2l.Any of the2d nearest grid neighbors from this layer can be found in O((log i)/d)using the algorithm described in[13].

In our analysis we essentially ignored all of the points from the layers below the i-th sample layer, since the number of them is not signi?cant.In prac-tice,it may be e?cient to use other layers for gener-ating nearest neighbors.Better bounds on dispersion and discrepancy may also be achieved then.

4.3Layered Sukharev Grid Sequence for

S d

Now,that we have de?ned a sequence for each of the spherical cubes,we need to de?ne an ordering in which all of the points from those sequences will be placed on the surface of the sphere.One straightfor-ward way to do this is to place one point from each

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of the faces’sequences at a time.The order in which each face should be considered is decided from the fol-lowing considerations.

Let the union of all of the spherical canonical rect-angles determine the range space for the whole sphere. Using the criterion of optimizing the discrepancy over the range space,the ordering of the?rst2(d+1)points for the resolution level0of the sphere can be explicitly computed.Hence,from this point on we can assume that we have such an ordering.Therefore,each next set of2(d+1)points from each of the sequences should follow the same ordering,since this will minimize the discrepancy over the range space.This will guarantee that Proposition4.3holds for the generated sequence on the sphere.

Our ongoing research is directed on proving that the same result holds for the larger range spaces,i.e., the ones that include combinations of the spherical rectangles from di?erent spherical cubes.

We can state the following result for the dispersion of the sequence,T s,on the sphere:

Proposition4.5The dispersion of the sequence T s

at the resolution level l containing n=2(d+1)·(2d(l+1)?1)/(2d?1)points is

dρ(T)≤

d

n(2d?1)2(d+1)+1

Proof:Applying the same argument as in the proof

of Proposition4.2,and considering that now2l= d n(2d?1)/(2(d+1))+1 /2,we obtain the de-sired bound.

5Experiments

We have implemented our algorithm in C++and applied to implementations of PRM-based planner[9] in the Motion Strategy Library.The experiments re-ported here were performed on a2Ghz Pentium IV running Linux and compiled under GNU C++.

Performance results are shown in Figures2, 3. The models that we designed are allowed only to ro-tate;therefore,the con?guration space is R P3.We compared the number of nodes generated by the ba-sic PRM planner using a pseudo-random sequence of quaternions[18],a pseudo-random sequence of Euler angles,and the layered Sukharev grid sequence.The results for pseudo-random quaternions and Euler an-gles sequences were averaged over50trials.When we tested

the deterministic sequence,we made sure

Random Random Layered Sukharev

Quaternions Euler Angles Grid Sequence 108830211067 Figure2:This problem involves moving a robot (black)from the north pole to the south pole.Mul-tiple views of the geometry of the problem are shown (obstacles are drawn in lighter shades)as well as com-parisons of the number of nodes generated by di?erent sampling strategies.

that each particular problem does not have any ad-vantage due to coincidental alignment with the grid directions of the sequence.Therefore,in each trial a ?xed,random quaternion rotation was premultiplied to each sample,to displace the entire sequence.The results obtained were averaged over50trials(a di?er-ent random rotation was used in each).

Based on our experiments we have observed that the performance of the deterministic sequence is equiv-alent to the performance of the random sequence for the PRM-based planner,which makes it an alterna-tive approach to random sampling.It is important to note,however,that for some applications,such as veri-?cation problem,only deterministic guarantees are ac-ceptable,making random sequences not appropriate.

The results we obtained for the problem in Fig-ure3using Euler angles emphasizes the importance of using quaternions and sampling in a way that re-spects the Haar measure.This problem was never solved using the random Euler angles.The experiment was running for several days,generated80000nodes, 6

Random Random Layered Sukharev Quaternions Euler Angles Grid Sequence 909>800001013

Figure3:In this example the goal is to move a robot along the https://www.doczj.com/doc/6d529820.html,parisons of the number of nodes generated by di?erent sampling strategies are shown.

but never found the solution.It is generally known that Euler angle parameterization has its drawbacks, such as gimbal lock and interpolation problems.How-ever,in motion planning,it has been a popular way to parameterize rotations.This example demonstrates the inadequateness of Euler angles parameterization. The interpolation method,ignoring the dependence between the three rotations(yaw-pitch-roll),tries to rotate around three axes simultaneously.In the con-?guration space with the narrow corridor this results only in those con?gurations that are in collision.

6Conclusions

We have proposed a general framework for perform-ing deterministic uniform sampling over spheres and SO(3).We have developed and implemented a par-ticular sequence which extends the layered Sukharev grid sequence designed for the unit cube.We have tested the performance of the sequence in PRM-like motion planning algorithms,which demonstrated that this sequence is a useful alternative to a random sam-pling.This is in addition to the advantages that this sequence has over random sampling,such as deter-ministic resolution completeness guarantees and the regular lattice structure.

There are many ways to improve the current work.

The spherical distortion grows with the size of the polytope faces and with the dimension.One improve-ment would be to use regular polytopes that have more faces.For example,for the case of SO(3),a600-face polytope exists(only300of them would be used be-cause of antipodal identi?cation).The di?culty is that our current approach would require sampling over

a simplex,as opposed to a cube.Another possibility

is to cut and unroll the(d+1)-dimensional polytope so that all of its d-dimensional faces form a connected subset of R d.It may then be possible to adapt a sam-pling method for rectangular subsets of R d to S d by rolling the polytope back up after sampling.

Another important direction of research is to de-termine how to combine deterministic sampling meth-ods for two spaces into a method over the Cartesian product space.For example,how can a sample se-quence developed for[0,1]3and another developed for SO(3)be combined to yield a good sequence for a six-dimensional con?guration space that corresponds to a set of translations and rotations for a3D rigid body?

In the case of random sampling,it is trivial to com-bine independent random samples;however,for deter-ministic methods,one must be very careful to avoid degeneracies.This is the reason,for example,why the Halton sequence[8]uses relatively prime integers as the basis for each dimension.

Acknowledgments We are grateful for the fund-ing provided in part by NSF CAREER Award IRI-9875304,NSF ANI-0208891,and NSF IIS-0118146 The layered sequence idea was developed by Steve Lin-demann and Steve LaValle in the context of[13].

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1简介2 2.4.3中文数字转换 (7) 2.5高级设置 (8) 2.5.1章节标题设置 (9) 2.5.2部分修改标题格式 (12) 2.5.3附录标题设置 (12) 2.5.4其他标题设置 (13) 2.5.5其他设置 (13) 2.6配置文件 (14) 3版本更新15 4开发人员17 1简介 这个宏包的部分原始代码来自于由王磊编写cjkbook.cls文档类,还有一小部分原始代码来自于吴凌云编写的GB.cap文件。原来的这些工作都是零零碎碎编写的,没有认真、系统的设计,也没有用户文档,非常不利于维护和改进。2003年,吴凌云用doc和docstrip工具重新编写了整个文档,并增加了许多新的功能。2007年,oseen和王越在ctex宏包基础上增加了对UTF-8编码的支持,开发出了ctexutf8宏包。2009年5月,我们在Google Code建立了ctex-kit项目1,对ctex宏包及相关宏包和脚本进行了整合,并加入了对XeT E X的支持。该项目由https://www.doczj.com/doc/6d529820.html,社区的开发者共同维护,新版本号为v0.9。在开发新版本时,考虑到合作开发和调试的方便,我们不再使用doc和docstrip工具,改为直接编写宏包文件。 最初Knuth设计开发T E X的时候没有考虑到支持多国语言,特别是多字节的中日韩语言。这使得T E X以至后来的L A T E X对中文的支持一直不是很好。即使在CJK解决了中文字符处理的问题以后,中文用户使用L A T E X仍然要面对许多困难。最常见的就是中文化的标题。由于中文习惯和西方语言的不同,使得很难直接使用原有的标题结构来表示中文标题。因此需要对标准L A T E X宏包做较大的修改。此外,还有诸如中文字号的对应关系等等。ctex宏包正是尝试着解决这些问题。中间很多地方用到了在https://www.doczj.com/doc/6d529820.html,论坛上的讨论结果,在此对参与讨论的朋友们表示感谢。 ctex宏包由五个主要文件构成:ctexart.cls、ctexrep.cls、ctexbook.cls和ctex.sty、ctexcap.sty。ctex.sty主要是提供整合的中文环境,可以配合大多数文档类使用。而ctexcap.sty则是在ctex.sty的基础上对L A T E X的三个标准文档类的格式进行修改以符合中文习惯,该宏包只能配合这三个标准文档类使用。ctexart.cls、ctexrep.cls、ctexbook.cls则是ctex.sty、ctexcap.sty分别和三个标准文档类结合产生的新文档类,除了包含ctex.sty、ctexcap.sty的所有功能,还加入了一些修改文档类缺省设置的内容(如使用五号字体为缺省字体)。 1https://www.doczj.com/doc/6d529820.html,/p/ctex-kit/

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中排布。年份、序号用阿拉伯数字标识,年份用全称,用六角括号“〔〕”括入。序号不用虚位,不用“第”。发文字号距离红色反线4mm。 (六)签发人 上行文需要标识签发人,平行排列于发文字号右侧,发文字号居左空一字,签发人居右空一字。“签发人”用3号方正仿宋_GBK,后标全角冒号,冒号后用3号方正楷体_GBK标识签发人姓名。多个签发人的,主办单位签发人置于第一行,其他从第二行起排在主办单位签发人下,下移红色反线,最后一个签发人与发文字号在同一行。 二、主体部分 (一)标题 由“发文机关+事由+文种”组成,标识在红色反线下空两行,用2号方正小标宋_GBK,可一行或多行居中排布。 (二)主送机关 在标题下空一行,用3号方正仿宋_GBK字体顶格标识。回行是顶格,最后一个主送机关后面用全角冒号。 (三)正文 主送机关后一行开始,每段段首空两字,回行顶格。公文中的数字、年份用阿拉伯数字,不能回行,阿拉伯数字:用3号Times New Roman。正文用3号方正仿宋_GBK,小标题按照如下排版要求进行排版:

tabularx宏包中改变弹性列的宽度

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2-1论文写作要求与格式规范(2009年修订)

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4.对本研究课题有创造性见解,并取得显著的科研成果。 5.学位论文必须是作者本人独立完成,与他人合作的只能提出本人完成的部分。 6.论文字数不少于5万字,中、英摘要3000字;详细中文摘要(单行本)1万字左右。 (四)临床专业学位博士论文要求 1.要求论文课题紧密结合中医临床或中西结合临床实际,研究结果对临床工作具有一定的应用价值。 2.论文表明研究生具有运用所学知识解决临床实际问题和从事临床科学研究的能力。 3.论文字数一般不少于3万字,中、英文摘要2000字;详细中文摘要(单行本)5000字左右。 二、学位论文的格式要求 (一)学位论文的组成 博士、硕士学位论文一般应由以下几部分组成,依次为:1.论文封面;2. 原创性声明及关于学位论文使用授权的声明;3.中文摘要;4.英文摘要;5.目录; 6.引言; 7.论文正文; 8.结语; 9.参考文献;10.附录;11.致谢。 1.论文封面:采用研究生处统一设计的封面。论文题目应以恰当、简明、引人注目的词语概括论文中最主要的内容。避免使用不常见的缩略词、缩写字,题名一般不超过30个汉字。论文封面“指导教师”栏只写入学当年招生简章注明、经正式遴选的指导教师1人,协助导师名字不得出现在论文封面。 2.原创性声明及关于学位论文使用授权的声明(后附)。 3.中文摘要:要说明研究工作目的、方法、成果和结论。并写出论文关键词3~5个。 4.英文摘要:应有题目、专业名称、研究生姓名和指导教师姓名,内容与中文提要一致,语句要通顺,语法正确。并列出与中文对应的论文关键词3~5个。 5.目录:将论文各组成部分(1~3级)标题依次列出,标题应简明扼要,逐项标明页码,目录各级标题对齐排。 6.引言:在论文正文之前,简要说明研究工作的目的、范围、相关领域前人所做的工作和研究空白,本研究理论基础、研究方法、预期结果和意义。应言简

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(公文写作)毕业论文写作要求和格式规范

中国农业大学继续教育学院 毕业论文写作要求和格式规范 壹、写作要求 (壹)文体 毕业论文文体类型壹般分为:试验论文、专题论文、调查方案、文献综述、个案评述、计算设计等。学生根据自己的实际情况,能够选择适合的文体写作。 (二)文风 符合科研论文写作的基本要求:科学性、创造性、逻辑性、实用性、可读性、规范性等。写作态度要严肃认真,论证主题应有壹定理论或应用价值;立论应科学正确,论据应充实可靠,结构层次应清晰合理,推理论证应逻辑严密。行文应简练,文笔应通顺,文字应朴实,撰写应规范,要求使用科研论文特有的科学语言。 (三)论文结构和排列顺序 毕业论文,壹般由封面、独创性声明及版权授权书、摘要、目录、正文、后记、参考文献、附录等部分组成且按前后顺序排列。 1.封面:毕业论文(设计)封面(见文件5)具体要求如下: (1)论文题目应能概括论文的主要内容,切题、简洁,不超过30字,可分俩行排列; (2)层次:高起本,专升本,高起专; (3)专业名称:现开设园林、农林经济管理、会计学、工商管理等专业,应按照标准表述填写; (4)密级:涉密论文注明相应保密年限; (5)日期:毕业论文完成时间。 2.独创性声明和关于论文使用授权的说明:(略)。

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