17th International Conference on Artificial Reality and Telexistence 2007
Construction of Virtual Assembly System with Real-Time Collision Detection Yuichi Tamura Naoki Mizuguchi National Institute for Fusion Science 322-6, Oroshi-cho, Toki, Gifu, Japan
[email protected]
Soju Matsumoto Heihachi Ueki 3D Incorporated 1-1, Sakae-cho, Kanagawa-ku, Yokohama, Japan
Abstract
interference detection engine called SmartCollision which can detect interference among polygons.
In the design of a large-scale device, it is very important to confirm that there is no interference or spatial conflict among the parts. Virtual reality devices have been used in various fields as design support systems, but detecting collisions between complex parts in real-time has been difficult. We have created a system that can detect collision and interference in real-time in a virtual reality system.
In this section, we show some algorisms installed to SmartCollision. Fig. 1 shows the concept of global penetration depth. Penetration depth is a concept relating to the amount of interference, and includes the direction in which an object moves. In the case of Fig. 1(a), current interference detection engines might divide object A along the dotted lines and calculate the interference among convex polyhedrons (in this case, among A1, A2, A3 and B). However, it is impossible to remove the interference in the case of Fig. 1(a) since the penetration depth vector of the A1 is opposite to that of object A3. We define the global penetration depth vector. Our algorithm does not divide concave objects into several convex objects. It calculates the penetration depth between object B and the complete object A.
1. Introduction Using a large-scale virtual reality (VR) system like CAVE for design purposes has been attempted in various fields. Generally, there may be two main reasons for doing so. One is to observe objects in real three-dimensional space, and the other is to assist in the assembly of parts. Numerous studies have been done in the former field ([1]), but research into parts assembly using VR lags behind. Although studies such as [2] did simulate the assembly process, they were limited to the construction of very small and simple structures. The aim of this study was to build a VR system with a collision and interference detection function that operates in real-time.
B B
B A3
A1
A
Global Penetration Depth Vector
A2
2. Interference Detection Engine
(a)
Figure 1. Global penetration depth vector
Research on collision and interference detection has been going on. One typical technique is to convert the 3-D shape model into voxel data form, and search for interference among these voxels. This method is relatively simple and easy to perform, but it cannot detect interference with a high degree of accuracy. More recently, other algorithms that can detect interference among complex objects have been proposed [3]. But even these methods have problems with concave shapes, and do not provide a strict solution. To solve such a problem, we constructed a VR system with an
0-7695-3056-7/07 $25.00 © 2007 IEEE DOI 10.1109/ICAT.2007.33
(b)
The interference in Fig. 2 sometimes occurs when an object enters the slot and rotates inside it. In this case, there are two contact or interference points. If we want to remove the interference between these objects only by translation of one object, the movement of the object may be very unnatural, as shown in Fig. 2(a). Of course, if we use both translation and rotation, we can remove the interference much more naturally as shown in Fig. 2(b). However, there is no
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4. Application Example and Evaluation
one, optimal solution for the combination of translation and rotation. The potential energy of the object is defined by the following expression:
We use the example of a nuclear fusion reactor design. As the construction period is ten years or more, numerous parts and devices will be added after the initial design has been completed. Thus, it is difficult to maintain consistency among parts, but it is very important to keep the system operational. Fig. 4 shows the example of applying this system to a nuclear reactor. The controllable object is the one in the center of this figure, to be installed in the reactor.
where means translation, means rotation, and k and mean stiffness about translation and rotation. We calculate the values of and which minimize the potential energy by the method of least squares.
Rotation Translation Contact Point
(a) Object not in contact (object is green)
(b)
(a)
Figure 4. Example
Figure 2. Translational penetration depth vector and rotational penetration depth vector
We evaluated this system. The total number of polygons in the models used for this evaluation was 385,806, and the total number of vertices was 96,942. In the evaluation, the total model size was fixed, and the number of polygons of the controllable and static objects was changed. The result is that the interference detection calculation is done at an average frequency of about 60 Hz, regardless of size. In the worst case scenario, however, calculation speed drops to 10 Hz or less. This occurs when the controlled object contacts several static objects at the same time. However this occurs only briefly, for 0.5 s even in the longest case.
3. System Configuration Our system consists of two networked computers, one for the virtual environment and the other for collision detection. This is because the cost of calculation of collision detection is high, and the refresh rate of the VR computer is becoming less efficient. The shape (object) data are shared beforehand, and objects are classified as those that can be moved, and those that are static in a configuration file. Fig. 3 shows the data flow between these two computers. The controllable object can be selected and changed by pushing a button on an input device. Using this function, we can freely assemble and test parts, one by one, to see whether these are suitable for a device or not.
5. Conclusion In this study, we proposed a system for simulating parts assembly. This system can detect interference among both convex and concave objects. We demonstrated how the system could be applied to the case of a nuclear fusion reactor. The speed of interference detection is about 60 Hz on average, much faster than the image refresh rate. On occasion the interference detection was much slower, but this was only for brief periods of well under 1 s.
CMP-1 VR Space
VR Computer
Information of Position and Rotation M = TR T: Translation R: Rotation C, C’: Model View Matrix P: Penetration Depth Matrix
(b) Object in contact (color changes to red)
SGI Onyx4
Inverse Matrix of Penetration Depth P -1
Collision Detection Computer C’M
References [1] V.D. Lehner and T. A. DeFanti, Computer Graphics and Applications, IEEE, 17(2), pp. 13-17, 1997.
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[2] T. Fernand, et al., Proc. 9th EUROGRAPHICS Portuguese Chapter, pp 43-49, 2000.
Figure 3. Data flow between the VR computer and the collision detection computer
[3] Y. J. Kim, et al., Proc. IEEE International Conferece on Robotics and Automation, 2002.
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