COMPUTER GRAPHICS

Volume 23 (2004), number 1 pp. 13–23

forum

Approximation of Optimal Voxel Size for Collision Detection in Maintainability Simulations within Massive Virtual Environments D. Borro1 , A. Garc´ıa-Alonso2 and L. Matey3 1 Department 2 Department

of Applied Mechanics, Ceit (Centro de Estudios e Investigaciones T´ecnicas de Guip´uzcoa), Spain [email protected] of Computation Science and Artificial Intelligence, University of the Basque Country, Spain [email protected] 3 Department of Applied Mechanics, University of Navarre, Spain [email protected]

Abstract This paper describes a Collision Method for massive virtual environments composed of millions of triangles. It has been applied in the aeronautics industry for maintainability simulations using virtual aircraft engine mock-ups. The method performs well and has a good interactive frame rate even when it is used for computing force feedback with haptic devices. Space sorting problems chiefly related to voxel techniques, such as memory requirements and optimal voxel size, have been solved. We use advanced memory structures and hashing techniques. To find the optimal voxel size, several analytical solutions have been proposed and compared. These solutions are based on the performance cost function of the algorithm used. Experiments have been undertaken to verify these analytical solutions. Keywords: Collision Detection, voxel techniques, haptic device. ACM CSS: I. 3. 6 [Computer Graphics]: Methodology and Techniques — Graphics data structures and data types; I. 3. 7 [Computer Graphics]: Three-Dimensional Graphics and Realism — Virtual Reality

Virtual reality provides an environment free of the high costs normally associated with physical mock-ups. As such, accurate and efficient collision detection is required in order to simulate maintainability tasks. Even more significant is the complexity and enormous size of the CAD input models (millions of triangles); so the visualization modules and the collision solver have to implement sophisticated techniques to obtain a real-time frame-rate.

1. Introduction Man-and-tool accessibility analysis, which is undertaken in order to analyse assembly-disassembly sequences and times, constitutes a critical area of research in the field of Maintainability. Nowadays the use of a physical mock-up at the developmental stage is mandatory in the evaluation of engine maintainability. Their use is also essential in other applications, such as training and demos. The expense involved with these mock-ups has led ITP (Industria de TurboPropulsores) to research an alternative, using virtual reality combined with haptic devices.

Moreover, the collision algorithm described in this paper also calculates a collision response, which is useful in giving force feedback by means of a haptic interface.

The device described here ensures that user movements are the same as those employed when testing physical mock-ups, providing an enhanced sense of real manipulation which can lead to important cost reductions in the development of new aircraft engines.

However, algorithms based on voxel techniques have several disadvantages, such as those related to memory storage requirements and the choice of voxel size. This paper introduces solutions to both of the above and should therefore make the use of spatial partition techniques more attractive.

 c The Eurographics Association and Blackwell Publishing Ltd 2004. Published by Blackwell Publishing, 9600 Garsington Road, Oxford OX4 2DQ, UK and 350 Main Street, Malden, MA 02148, USA.

Submitted May 2003 Accepted November 2003

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D. Borro et al. / Approximation of Optimal Voxel Size for Collision Detection

The paper is organized in the following manner. Section 2 presents a state-of-the-art overview and a brief classification of collision problems. Section 3 describes the specific problem involving the simulation of maintainability task using virtual aircraft engine mock-ups. In Section 4 the description of the collision algorithm used for collision detection is presented. A current problem in collisions using spatial enumeration is the choice of an optimal cell size. This problem is analysed and solved in Section 5. The Collision Solver has been integrated as part of an industrial system named REVIMA. Section 6 briefly describes some experimental results. Finally, in Section 7, conclusions are presented.

2. Related Work A lot of research on the problem of collisions has been published over the past two decades. Because the application area is quite wide, several interesting papers have provided brief surveys about different problems and methods of collisions [1, 2, 3, 4]. In Collision Detection considerable effort could be needlessly expended checking polygons that don’t actually collide. So, almost all techniques use structures which succeed in efficiently rejecting most unnecessary computations as easily as possible. These methods can be separated into several groups: Bounding Volumes, Spatial Partition or Subdivision techniques, Volume Hierarchies methods and Proximity methods. Bounding Volumes enclose the scene objects within simple geometries, so the collision check among these geometries is very easy and efficient. These kind of techniques are used above all in n-body problems with Sweep and Prune methods [5, 6] to avoid the O(n2 ) of testing all objects. Spatial Partition techniques divide the scene space in smaller volumes. Before testing the scene polygons, one test is done very quickly among the small volumes to reduce the final number of polygons to test. The subdivision can be uniform with voxels [7, 8] or hierarchical with octrees [9, 10], k-d trees [8, 11] or sphereTrees [12, 13].

However, most published research results deal with smallto-medium scenarios involving only thousands or hundreds of thousands of polygons. If the application requires the management of massive models with millions of polygons, it is necessary to improve these techniques to obtain low memory storage and a real-time performance. For example, Wilson et al. [24] stored the whole scene in secondary memory. Then, they built an overlap graph of scene objects and only loaded in cache memory those sub-graphs that had probabilities of having objects in collision. This solution exploited the spatial coherence of the scene. Other researchers, from Boeing [25], have suggested a new voxel-based method that differs from other existing voxel methods. For the static objects they have substituted the polygonal model for a grid of voxels called a voxmap, whilst the mobile objects have been represented by a set of surface point samples called a point shell. This offers a very attractive method of calculating the penetration between two objects.

3. Description of the Problem Our problem needs a collision detection module. The virtual mock-ups contain millions of triangles and we need interactivity to simulate real maintainability tasks. Besides the generation of images for the simulation, it is necessary to offer a tactile feeling to the user. This feeling is the key to realistically simulating maintainability tasks. To achieve it, the system has a haptic device that can relay a feedback force when a collision is detected. The above kind of environment is quite different from others such as path finding, robotics, vehicles, virtual walks, and even mechanism design, that also deal with the collision problem. The B-rep geometric model is used to represent the scene objects and then a triangulation process is run in case the file format has any other sort of polygon. Our application considers three kinds of objects:

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In contrast to the above, the subdivisions of Volume Hierarchies methods are not based on space. They subdivide the objects or polygons using their bounding volumes and then group them, thus building a tree. AABBTrees [8, 14], OBBTrees [6, 15, 16], SphereTrees [1, 17, 18] or k-DOPTrees [19, 20] can be constructed, depending on the bounding volume used.

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The final group uses the geometrical neighbourhood amongst objects to realize proximity queries. In addition to using spatial and temporal coherence, Proximity methods combine with totally different techniques, such as those methods using Voronoi Regions [5, 21, 22] or Swept Spheres [23].

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Virtual mock-up (static object): this object merges thousands of objects, all of which taken together compose the virtual mock-up of the aeronautic engine. In the literature, these kinds of models are called massive models because they can have a great number of polygons. Figure 1 shows a graphical example of the complexity of a virtual engine mock-up. Virtual tool (mobile object): the worker, who is using the haptic interface, moves a virtual tool across the scene to simulate the maintainability tasks. It can have hundreds of polygons or several thousands. Disassembled object: in a simulation the hand or a tool can grip only one object. It can be any engine part (bolts, nuts, pipes, etc.).

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D. Borro et al. / Approximation of Optimal Voxel Size for Collision Detection

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Figure 2: The general design of the proposed method. Figure 1: 3D model of an aeronautic engine mock-up. 4.1. Pre-Process Block To sum up, our collision problem has two specific characteristics. The first one concerns the number of scene objects. There is a reduced number of mobile objects (the tool or some engine part), while the rest of the scene is a large set of static objects. Therefore, the mobile objects represent only about 0.5% of the total number of facets in the scene. The second concerns the relative position between the static and mobile geometry: the facets of the mobile object are usually close to a subset of static facets.

This block has to generate data structures which are useful in obtaining a good frame rate in the runtime block. The static objects (virtual mock-up) in our algorithm, have been treated collectively as a unique solid when building the partition model, rather than considering each, with its corresponding voxel decomposition, individually. In the case of mobile objects, geometric information is used directly, i.e. it has no voxel structure, because in these configurations traversing polygons directly is more efficient.

The compact nature of the virtual model and its high number of polygons, forces us to use spatial sorting techniques. We have chosen techniques based on voxels, but we have also tested hierarchical methods like octrees. Although these required less memory storage, the computation time was higher than that needed for direct access voxel techniques.

This block is divided into three stages:

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r 4. Description of Collision Detection Algorithm The collision algorithm uses spatial partition techniques to solve the collision detection problem. In our case, the spatial partition model generated is only used to reduce the problem complexity, so the original geometric model is not replaced. Figure 2 shows the general design of the collision algorithm. In a pre-process block, a partition model is generated from the B-rep model. Next, at runtime, the partition model is used to detect the set of voxels in collision and to accomplish interference checks between triangles. Following the nomenclature of Hubbard [12], these phases are named broad phase and narrow phase.

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Computation of the optimal voxelization: this stage is very important in order to ensure that the system performs well. It is necessary to guess which the best subdivision is. Generation of voxel grid: when the optimal subdivision has been calculated, this stage has only to build the data structures that will efficiently store the voxel grid and certain others structures. Associating triangles with voxels: this stage finds and connects which triangles are intersecting with which voxels.

The first point is discussed in Section 5. The two last stages are described below, assuming that the optimal voxelization is already known.

In addition, a zone (defined by the dotted arrow) is shown in the scheme. This zone refers to the collision response computation used to provide a feedback force. The research related to this topic has been already reported [26].

The voxel grid generation stage subdivides the initial scene volume or AABB and generates our voxel based data structure. During this stage the Voxel array is built (see Figure 3) as an array of pointers to VoxTri objects (Voxel-Triangle). This representation ensures minimal information per voxel (only 4 bytes per voxel). The VoxTri array has a list of pointers to triangle data.

The next two subsections give a description of the general design blocks.

Each triangle requires an instance of the class TriCol. It has pointers to the vertex coordinates and the plane equation that  c

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D. Borro et al. / Approximation of Optimal Voxel Size for Collision Detection

Figure 3: Memory representation of data structures used by the collision algorithm.

Figure 5: Perfomance of different hashing levels.

where N i × N j × N k determine the virtual voxel structure size.

Figure 4: Allocated memory of the different data structures for different edge sizes of voxels.

contains the triangle. It also has other information, which is very useful to accelerate computations in the runtime phase. Each TriCol object uses 42 bytes. Figure 3 shows the different data structures used to voxelize the static object (the mobile object only has the TriCol array). Figure 4 presents an example of memory consumption for a model of 1.6M polygons, using voxels of different sizes. This is our original method. However, for a large number of voxels, the memory consumed can require too much storage. This can happen with very large models of more than one million polygons. To save memory, a Hash Table has been implemented. This technique, often used in data base management [27], transforms one virtual table (in our case the voxel array) into another smaller table. There is a function that receives the key (in our case computed from the i, j, k positions of one voxel) and returns the address of a record (voxel) that contains the data associated to that key. Although a few authors say they use hash tables [28], they neither provide much information nor results. We have selected a well-known Hash function often used in the Data Base field. key = i · N j · Nk + j · Nk + k H (key) = key% (newT ableSi ze)

(1)

The computing cost of using hashing techniques is shown in Figure 5. The hashing level axis refers to the percentage of memory saved. The time axis includes all collision computing. The figure can be examined to find a compromise between performance and memory storage. Using the hashing level as a parameter, we can easily control the amount of memory consumed by the Voxel array, which is the critical memory consumer when voxel volume decreases (see Figure 4). Figure 5 shows how savings up to 70% can be selected without increasing significantly the computational cost of collision detection algorithms. The test plotted corresponds to one real engine with 1.6M polygons. In all our tests we have achieved similar results, being the 70% hashing level (i.e. 70% memory saving) the limit for memory savings. Section 6 provides results for different environments. The last pre-process stage identifies, for each triangle, the voxel or voxels that intersect. The Bibliography provides several methods that detect the interference between a polygon and a parallelepiped. We have implemented the AkenineM¨oller [29] method, which seems to be the most efficient.

4.2. Runtime Block An earlier work [7] considers the use of Voxel structures for all objects. This solution offers good results in n-body applications but in our case the mobile object will always be inside the static volume. We must ask why the mobile object needs to be partitioned, if ultimately it is boundary (triangular) precision that we are seeking to achieve. In fact, our algorithm is more similar to the work of Held et al. [8] but in our case we can get rid of their first step.  c

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D. Borro et al. / Approximation of Optimal Voxel Size for Collision Detection

Therefore, our method goes directly to the object geometry structure. The runtime block can be divided into two phases: broad phase and narrow phase. In n-body problems, the broad phase avoids the quadratic complexity of checking all the object couples. In our case, the broad phase finds the primitives (triangles) with probabilities of collision and the interest space is reduced from the whole scene to a small set of objective voxels.

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give visual, acoustic and tactile feedback. Visual and acoustic feedback is easily achieved but the feedback force is not so easy to obtain. A second collision module, the Collision Response, is necessary. This is given the job of calculating the required parameters, such as the contact plane and the penetration between the two objects [26].

5. The Optimal Search Problem For each triangle t of the mobile object, we calculate the set of objective voxels, vo , intersected by the AABB of t. vo = {v ∈ V | v ∩ A AB Bt = φ}

(2)

where V is the set of static scene voxels and AABB t is the bounding box of triangle t. Next, the narrow phase is run for this triangle and its vo . When a mobile triangle t has a non empty set of V o,t associated with it, then the narrow phase is run. This consists of checking t for intersection with all triangles contained in V o,t . In fact, the loop broad-narrow is run as many times as the number of mobile object triangles. The fast M¨oller [30] narrow phase algorithm is used to check the intersection between two triangles. In our implementation, this algorithm makes an average of 105 overlap tests per second. Other solutions based on voxels use cell precision [25]. However, our implementation reaches the triangle level, which is needed for the contact analysis required by the haptic control system. However, in this phase a filtered stage can be introduced before checking the two triangles with the M¨oller algorithm. We have implemented two filters: detection of repeated computations and discrimination using triangle AABBs.

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This section describes in detail the problem of determining the optimal voxel size for a collision detection problem.

Detection of repeated computations: Using one flag (frame id) and the triangle address, it is possible, in each frame, to detect if a triangle-triangle check has been accomplished before. This filter means 4 bytes more per triangle in the TriCol array, but does not represent a significant impact in terms of memory storage (this addition has already been taken into account in Figure 4). This approach is similar but not identical to that of Redon et al. [31]. Discrimination using triangle AABBs: Before applying the M¨oller algorithm, a rapid check is made between the AABBs of the triangle couple. This is a very rapid test and identifies many unnecessary computations.

Thanks to these filters, the performance of collision detection is increased by 50%. The output of the algorithm is two sets of triangles colliding (one belonging to the static object, the other to the mobile object). Thanks to this information, it is possible to

Spatial partition techniques present a particular problem: finding a voxel size that provides the best simulation time possible. If the voxel grain is too large compared with the size of the scene triangles, then each voxel will contain many polygons, increasing the time needed to scan all the triangletriangle couples. Meanwhile, a voxel size, which is too small, provokes two problems: the first relates to the high number of voxels that subsequently appear in the broad phase, and the second to the repeated checks needed between triangle couples. The above cases represent the two extremes and the idea would be a procedure that finds the best voxel size for any model automatically. The problem is that this size depends on many factors and it is quite difficult to find an analytical method [28, 32]. Whilst several works have raised the optimal size problem, with all authors proposing solutions based on experiments, none have proposed an analytical solution. For example, Held et al. [8] say that for their models, a voxel optimal range could be between 5 × 5 × 5 and 50 × 50 × 50 levels (where each number refers to the number of voxels in each dimension). Garc´ıa-Alonso et al. [7] propose an OBB for each scene object. These OBBs are subdivided into voxels. In their experiments, they found an optimum for 512 voxels per OBB. Another solution is that offered by McNeely et al. [25]. In this work, the space discretization depends on the resolution that they want to obtain. In their experiments they worked with 5 mm resolution. Gregory et al. [28] give two approximations to the problem: one experimental and the other, analytical. In the first one they set the voxel size to the average length of all scene edges multiplied by a constant, which was chosen on the basis of experimentation. In the analytical solution, they give a set of assumptions to work with their models. For example; the last and current tool location are close to each other (the path swept goes through one voxel approximately), the model is uniformly tessellated and there is only one contact point between the model and the tool. With these assumptions, they can calculate the cost of performing three different algorithms: one based on OBBtree C r , another based on a hashing scheme with voxel partition  c

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D. Borro et al. / Approximation of Optimal Voxel Size for Collision Detection

C g , and the last, a hybrid method based on the two algorithms before C h . The hybrid algorithm is more efficient than the others only when two inequalities are satisfied: C h < C r and C h < C g . This means that the average number of triangles per optimal voxel size will be bounded by two values. However, their conclusion is that it is rather difficult to determine one unique value for both the optimal size and for all scenarios. In our case, this solution cannot be used. The first reason is that the solution by Gregory et al. is based on their algorithms and methods. The other reason concerns their assumptions. They are not valid here. In our application, the last and current tool locations can be close or not close between two computation cycles. Our CAD/CAM models are not uniformly tessellated because they are directly exported from CAD applications. They can be uniformly tessellated but memory requirements and computation costs would be increased. Neither is the contact point unique because we are working with 3D tools, contrary to typical haptic applications that use only one point for tool location. Therefore, we need another analytical solution that approximates an optimal voxel size for our compact scenarios. The heuristic solution found is presented in the next two subsections. The results of the analytical solution are verified by comparing them with experimental results in Section 5.3. 5.1. The Cost Function of the Algorithm The factors that we have considered in the solution are the following ones: the number of polygons in static and mobile objects, the average area and edge length of the polygons, and the container volume of the static object. Tool location or contact volume, could affect the cost function. The worst case would happen if the moving object would be immersed within the static parts. We do not consider this situation because the haptic system prevents it. So when analysing voxel optimisation we consider “nominal” working conditions: the moving tool penetrates the static object but not too much. Before going on, it must also be said that we have made experiments with different voxel geometries: parallelepipeds and cubes. Our results showed no performance difference (for the same memory usage) between the parallelepiped and the cubic shape. The tested parallelepiped shapes were not too flat or too slender: sides ratio no bigger than 2. The total computation time depends on the algorithm used to detect the collisions. So, the strategy to find the optimal size will consist of taking the algorithm cost function and searching for its minimum. The algorithm cost function will be the function that approximately describes the algorithm’s behaviour (number of loops, times, etc). Now onwards, we will explain how we arrived to its definition as the function of the voxelization cell size. Once we have a cost function, we can determine which voxelization makes its minimum.

As explained in Section 4.2, the algorithm has two main levels of precision: the voxels level and the triangle level. Each mobile object triangle goes through both levels. So, if n m is the number of mobile triangles, vo is the mean number of voxels covered by the AABB of a triangle and f s is the number of static object facets per voxel, the cost function z that describes algorithm behaviour might reasonably be expected to be: z = n m · vo · f s

(3)

However, this formula considers only the worst case. That is, when all mobile triangles are colliding with non-empty voxels. In a normal simulation, the conditions will be no collision or the tool will collide with only a few triangles. So, we have separated z into two factors, each one corresponding to each level of precision. z = n m · pnc · vo + n m · (1 − pnc ) · vo · f s

(4)

where p nc is the probability (estimated average) of no collision between one mobile triangle and one not empty static voxel. In our experiments with simulations of maintainability tasks, when the tool is slightly colliding with the static surface, the percentage of mobile triangles that collide with non-empty static voxels is in the 1–5% range. This means that p nc is within the 0.95–0.99 interval (in our experiments we have used p nc = 0.99). In (4), vo and f s have the same weight but this is actually not correct because the computation time to check the intersection between two triangles is significantly greater than the time consumed looking to see if a voxel is full or empty. The ratio between voxel and facets times, t f /tv , is called r t . And taking into account that n m is a constant factor, the final algorithm cost function is: z = pnc · vo + (1 − pnc ) · vo · f s · rt

(5)

According to our own experiments, the computation time of f s level, t f , is approximately a hundred times greater than tv . However, we still have to express vo and f s as the function of the voxel size (variable) and scene geometric data (specific geometric parameters). 5.2. Cost Function Parameters Here we derive an expression of the cost function for obtaining the voxel size (δ) that minimizes the function value. vo and f s values for an specific scene and voxelization can be directly evaluated. However, when defining a cost function they must be approximated (guessed) as the function of a variable (δ) and scene geometric parameters. The objective voxels, vo , are the number of voxels intersected by a mobile object triangle. According to the algorithm, this set is the set of voxels intersected by the AABB of  c

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D. Borro et al. / Approximation of Optimal Voxel Size for Collision Detection Table 1: Number of polygons for static and tool models

Figure 6: Triangle with greatest area contained in a voxel. triangle t. Because the triangles can be in any space orientation, the AABB of t, in the worst case, will be a cube in which the side, l AABB , is equal to the largest edge of the triangle. When the AABB of the worst case is calculated, it is easy to predict (also in the worst case) the number of voxels intersected by it. 3  l A AB B vo = +2 (6) δ The parameter f s , the average number of static facets per voxel, can be approximated (guessed) using different methods. For example, using the area, the greatest triangle contained in a voxel is the triangle built with the diagonals of the three voxel orthogonal facets as shown in Figure 6. The area of this triangle, Avox , can be calculated using the cross product between a and b. a·h   ∧ Avox = 1 1 2 ⇒ Avox = · a · b · sin(ab) = · |a × b| ∧  2 2 h = b · sin(ab) ⇓ 1√ 4 (7) Avox = 3δ 2 And finally, if A s is the average area of the static object triangle, the prediction of the number of static object facets per voxel based on area triangles, f s,area , is: √ Avox δ2 3 f s,ar ea = = (8) As 2As Gregory et al. used the average edge length in their formula, so we have also considered the average edge length of the static object triangles (E s ). Using E s and, either the voxel diagonal or the voxel average side length, we obtain two other formulas that approximate f s :  δx2 + δ 2y + δz2 diagvox f s,diag = = Es Es sidem,vox δ x + δ y + δz f s,side = = (9) Es 3E s

Tested Static models

Polygons

Tested Tool models

Polygons

Carcass Differential Turbine Externals Engine Tay

51782 149119 711014 841091 1615172 3571682

Torque Wrench Square Tool-Hand

544 770 954 2687

Going back to the cost function (5) vo and f s are predicted with these formulas, n m is an input data that is directly obtained from the tool model used. As it has been already said, p nc and r t are constants obtained in typical maintainability experiments. As it is easy to generate cubic voxelizations, here we will consider only this case. So, the optimal subdivision will be the actual voxel size (δ a ), which brings a minimum value of the cost function. The optimal voxel size δ a is obtained in the pre-process stage and the time required evaluating the cost function is negligible. We find δ a solving the equation (10). dz =0 dδ

(10)

In the next subsection, real experiments have been accomplished to verify the analytical results using different combinations of static and tool models. 5.3. Verifying the Analytical Solution The different mock-ups and tool models used in the experiments are shown in Table 1. It should be noted that in industrial applications, although static models can be very different, tool models are very similar in the number of polygons. The model “Tool-Hand” is a worst case because a tool and a hand models compose it. Specific software has been developed to verify the results obtained for δ a . This software automatically runs an analysis for a range of voxel sizes. In each analysis the tool is positioned in different work locations (10 positions in total). So, the automatic analysis makes 10 computations for each voxel size. The average of these ten samples is computed. Figure 7 shows the average time when using different voxel sizes. The average time point set is fitted by a polynomial function f (x) (in this case of 5th degree). In each experiment we evaluate an optimal zone ([s min , s max ]) which is an interval around the optimal size (s opt ) found in each experiment. Any voxel size within that interval can be considered as a sufficient optimal guess. The minimum value of f (x) is used to give the optimal size and then the optimal zone is established using values close (+1 ms) to f’(x) = 0.  c

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D. Borro et al. / Approximation of Optimal Voxel Size for Collision Detection Table 2: Values of ε δ with cube voxel grid Tool Model

Figure 7: Experimental optimal level and the optimal zone with the “Engine”-“Hand” couple.

Method

Static

Torque

Wrench

Square

Hand

Area

Carcass Differential Turbine Externals Engine Tay

0.60 0.36 0.81 0.70 0.77 0.32

0.40 0.27 0.97 0.67 0.36 0.85

0.25 0.46 0.73 0.58 0.67 0.10

0.41 0.49 0.73 0.43 0.64 0.07

Diagonal

Carcass Differential Turbine Externals Engine Tay

1.47 1.16 1.39 1.22 1.56 1.80

0.04 0.87 1.75 1.42 1.14 0.16

1.08 1.00 1.45 1.31 1.65 1.12

1.93 1.31 2.04 1.66 1.88 2.78

Side

Carcass Differential Turbine Externals Engine Tay

1.55 1.23 1.39 1.22 1.56 1.97

0.71 1.15 2.16 1.80 1.54 1.10

1.17 1.10 1.45 1.31 1.79 1.27

2.14 1.48 2.15 1.87 2.12 3.29

For each experiment, we compare the optimal voxel size (δ a ) guessed by our own formulations with the optimal zone obtained by the experiments. We can contrast the analytical and experimental solutions if a voxel size error, ε δ , is defined.   δopt − δa  · δopt + δa δa δopt



(11) εδ = δopt a (δopt − δmin ) · δa + (δmax − δopt ) · δδopt If ε δ is zero then δa is the exact optimal size. If 0 < εδ ≤ 1, δ a is not the optimal but it is inside the optimal zone. Table 2 shows the different values of ε δ for each model couple. It shows that the analytical solution based on area makes a very good approximation to the optimal voxel size. Bad approximations (grey cells) correspond to edge methods, which do not provide such a good approximation, so we reject them. The results obtained with parallelepiped voxels are similar.

6. REVIMA Application and Experimental Results The collision library is used in a multidisciplinary system named REVIMA (Virtual Reality for Maintainability) [26]. This is a haptic system developed to check the maintainability of aircraft engines. Amongst other disciplines, it includes mechanical design, control theory, computer graphics, computational geometry and human-computer interaction. All of them have been totally development at the CEIT Applied Mechanical Department. The REVIMA challenge was the mechanical design and construction of our haptic LHIfAM (Large Haptic Interface for Aeronautic Maintainability). The haptic device provides a workspace similar to the size of a turbo-engine (Figure 8). The system provides six measured degrees-of-freedom (DOF): three for translation and three for orientation. The force feedback is only actuated in the three translational DOF.

Figure 8: User with LHIfAM in a virtual maintainability operation on a CAD aircraft engine model. The software has been developed in Visual C++, OpenGL and Microsoft Windows 2000 OS. A unique processor is dedicated to the Collision Solver (866 MHz Pentium III Xeon). Another identical processor is used to manage the Graphical User Interface (GUI) and the visual representation. 2 Gigabytes of RAM are available to store the scene geometry and spatial partition structure. Using this hardware all the configurations tested in Table 2 achieve at least 10 Hz, sufficient for an interactive frame rate [33]. This comprises solving the collision problem and computing the force response required by the control module  c

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Figure 11: Memory consumed with different methods.

Figure 9: Performance of collision module depending on the size of static models.

pendence on the mobile geometry (see Section 4.2). So, in Figure 10 we present experiments with tools modelled with high precision levels. Anyway, the tools modelled with hundreds of polygons are enough to obtain the accuracy required in simulations. Following the study in Section 4.1, Figure 11 compares memory consumption with a 70% hashing and without it. As it was said, our experiments show that a 70% hashing can be used without a significant loss of performance.

Figure 10: Performance of collision module depending on the size of mobile models.

The memory consumed can be compared with other methods published by McNeely et al. [34]. These works, accomplished at Boeing, provide experiments with their software VPS and the freeware V-Collide [6]. Our method consumes more memory than VPS but less than V-Collide. It should be noted that, our data structures, as those of V-Collide, make use of the original surface data, but VPS relies on the voxel geometric model. 7. Conclusions

to provide force feedback. It should be noted that the haptic control loop runs faster and that the visual refresh rate and the collision module run independently in a dual processor. Different techniques have been studied and applied by the authors to solve the asynchronous communication problem [26]. Figure 9 shows the performance of collisions depending on the size of static models. The collision time concerns the total collision time: broad and narrow phases and the computation of the collision response (this last computation has a cost less than 1 ms [26]). The trendline is fitted by the least squares method. These experiments are accomplished in normal conditions, i.e. the tools are modelled in the same way (hundreds of polygons). However, the proposed algorithm has a great de-

We have described the specific problem of collisions that can be found in maintainability task simulations. We have studied approaches to the collision problem in different environments. Our studies lead to the design and optimisation of an algorithm that uses spatial partition techniques using voxels. The performance of the algorithm has been shown and it has been verified that it achieves the specifications of interactivity needed for maintainability simulations. Most authors use experimental data to select voxel size. In this work, we have developed and validated an analytical method based on a cost function. Our solution is dependent on our algorithm but a cost function for other algorithms can be obtained in a similar way. We have had to study in depth the problem of model storage memory. We also present an original study of hashing behaviour.  c

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ference on Computer Geometry, Vol. 3, pp. 205–210, Qu´ebec City, Qu´ebec, Canada. August 1995.

Finally, all of the above plus investigations from different authors have been integrated and applied to an industrial project in the aeronautics industry.

9. C.A. Shaffer and G.M. Herb. A Real-Time Robot Arm Collision Avoidance System, IEEE Transactions on Robotics and Automation, Vol. 8, No. 2, pp. 149–160. April 1992.

Acknowledgements The authors would like to thank to ITP and Sener for promoting and funding the application REVIMA. This application has been partially financed by the project of the Basque Government, number CI01TP03. We would like to especially thank REVIMA Group: Iker Aguinaga, Aiert Amundarain, Joan Savall, Jorge Juan Gil, Emilio S´anchez, Jaime Rub´ı, Javier Mart´ın Amezaga, Isa´ıas Cerrillo, Angel Rubio and Imanol Puy for their effort in the development of this work.

References 1. P.M. Hubbard. Approximating Polyhedra with Spheres for Time-Critical Collision Detection, ACM Transactions on Graphics, Vol. 15, No. 3, pp. 179–210. July 1996. 2. M.C. Lin and S. Gottschalk. Collision detection between geometric models: a survey, Proceedings of IMA Conference on Mathematics of Surfaces VII, Birmingham. August 1998. 3. P. Jim´enez, F. Thomas and C. Torras. 3D collision detection: a survey, Computer & Graphics, Vol. 25, No. 2, pp. 269–285. April 2001. 4. C. O’Sullivan, J. Dingliana, F. Ganovelli and G. Bradshaw. Collision Handling for Virtual Environments, Eurographics 2001 Tutorial Proceedings, 2001. 5. J.D. Cohen, M.C. Lin, D. Manocha and M.K. Ponamgi. ICOLLIDE: An Interactive and Exact Collision Detection System for Large-Scale Environments, Proceedings of ACM Interactive 3D Graphics Conference, Vol. 1, pp. 189–196. 1995.

10. A. Smith, Y. Kitamura, H. Takemura and F. Kishino. A Simple and Efficient Method for Accurate Collision Detection Among Deformable Polyhedral Objects in Arbitrary Motion, Proceedings of the IEEE Virtual Reality Annual International Symposium, pp. 136–145, North Carolina, USA. March (11–15) 1995. 11. G. Zachmann. Real-Time and Exact Collision Detection for Interactive Virtual Prototyping, Proceedings of DETC’97 (ASME Design Engineering Technical Conferences), September 14–17, Sacramento, California. 1997. 12. P.M. Hubbard. Interactive collision detection, In Proceedings of the 1993 IEEE Symposium on Research Frontiers in Virtual Reality, pp. 24–31. October 1993. 13. I.J. Palmer and R.L. Grimsdale. Collision Detection for Animation using Sphere-Trees, Computer Graphics Forum, Vol. 14, No. 2, pp. 105–116. 1995. 14. G. Van Den Bergen. Efficient Collision Detection of Complex Deformable Models using AABB Trees, Journal of Graphics Tools (JGT), Vol. 2, No. 4, pp. 1–13. 1997. 15. S. Gottschalk, M.C. Lin and D. Manocha. OBBTree: A Hierarchical Structure for Rapid Interference Detection, Proceedings of the ACM SIGGRAPH’96 – Computer Graphics, pp. 171–180. 1996. 16. S. Redon, A. Kheddar and S. Coquillart. Fast Continuous Collision Detection between Rigid Bodies, Computer Graphics Forum (Proceedings of the Eurographics 2002), Vol. 21, No. 3, pp. 279–287, Saarbr¨ucken, Germany. September (2–6) 2002.

6. T.C. Hudson, M.C. Lin, J. Cohen, S. Gottschalk and D. Manocha. V-COLLIDE: Accelerated Collision Detection for VRML, Proceedings of the second symposium on Virtual Reality Modeling Language (VRML’97), pp. 119–125. 1997.

17. S. Quinlan. Efficient Distance Computation between Non-Convex Objects, Proceedings of the IEEE International Conference on Robotics and Automation, Vol. 4, pp. 3324–3329. San Diego, CA, USA. 1994.

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8. M. Held, J.T. Klosowski and J.S.B. Mitchell. Evaluation of Collision Detection Methods for Virtual Reality FlyThroughs, Proceedings of the Seventh Canadian Con-

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Conference on Intelligent Robots and Systems, pp. 2887– 2892, EPFL, Lausanne, Switzerland. September (30) – October (4) 2002.

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23. E. Larsen, S. Gottschalk, M.C. Lin and D. Manocha. Fast Proximity Queries with Swept Sphere Volumes, Technical report TR99-018, Department of Computer Science, University of North Carolina, Chapel Hill. 1999. 24. A. Wilson, E. Larsen, D. Manocha and M.C. Lin. Partitioning and Handling Massive Models for Interactive Collision Detection, Computer Graphics Forum (Eurographics’99), Vol. 18, No. 3, pp. 319–329, Milan, Italy. September (7–11) 1999. 25. W.A. McNeely, K.D. Puterbaugh and J.J. Troy. Six Degree-of-Freedom Haptic Rendering Using Voxel Sampling, Proceedings of the ACM SIGGRAPH’99 Computer Graphics, pp. 401–408. Los Angeles, California, USA. August 1999. 26. J. Savall, D. Borro, J.J. Gil and L. Matey. Description of a Haptic System for Virtual Maintainability in Aeronautics, Proceedings of the 2002 IEEE/RSJ International

30. T. M¨oller. A Fast Triangle-Triangle Intersection Test, Journal of Graphics Tools (JGT), Vol. 2, No. 2, pp. 25– 30. 1997. 31. S. Redon, A. Kheddar and S. Coquillart. CONTACT: Arbitrary in-between motions for collision detection, Proceedings of the IEEE International Workshop on Robot-Human Interactive Communication (ROMAN). 2001. 32. D. Zhang and M.M.F. Yuen. A Coherence-based Collision Detection Method for Dressed Human Simulation, Computer Graphics Forum, Vol. 21, No. 1, pp. 33–42. 2002. 33. A. Amundarain, D. Borro, A. Garc´ıa-Alonso, J.J. Gil, L. Matey and J. Savall. Virtual Reality for Aircraft Engines Maintainability, Proceedings of Virtual Concept 2002, pp. 60–65. Biarritz, Francia. 9–10 Octubre 2002. 34. Web page of VPS Memory Comparison: http://www. boeing.com/phantom/vps/extra/memory.html.

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