Computation of penetration between smooth convex objects in three‐dimensional space

There are many applications in robotics where collision detection, separation distance, and penetration distance between geometrical models of objects are required. Efficient numerical procedures for the computation of these proximal relations are important as they are frequently invoked. A new measure of penetration and separation called the growth distance has been introduced in the literature. It has been shown that the growth distance can be efficiently computed for convex polytopes. This article extends the computation of growth distance to smooth convex objects. Specifically, we introduce a formulation of the growth distance for smooth convex objects that is well suited for numerical computation. By modeling a convex object as union of convex subobjects, the growth distance of a wide family of objects can be computed. However, computation of growth distance for such object models may be expensive. A fast algorithm is introduced that reduces the computational time significantly. In the case where the objects undergo continuous relative motions and the growth distances must be evaluated for a large number of closely-spaced points along the motions, further reduction in computational effort is achieved. Numerical experiments with objects that are found in typical robot applications substantiate the claim. © 1996 John Wiley & Sons, Inc.

[1]  Elmer G. Gilbert,et al.  New distances for the separation and penetration of objects , 1994, Proceedings of the 1994 IEEE International Conference on Robotics and Automation.

[2]  David G. Kirkpatrick,et al.  A Linear Algorithm for Determining the Separation of Convex Polyhedra , 1985, J. Algorithms.

[3]  K Sridharan,et al.  Measures of intensity of collision between convex objects and their efficient computation , 1991 .

[4]  S. A. Cameron,et al.  Determining the minimum translational distance between two convex polyhedra , 1986, Proceedings. 1986 IEEE International Conference on Robotics and Automation.

[5]  Nimrod Megiddo,et al.  Linear Programming in Linear Time When the Dimension Is Fixed , 1984, JACM.

[6]  Charles E. Buckley,et al.  A Foundation for the "Flexible-Trajectory" Approach to Numeric Path Planning , 1987, Int. J. Robotics Res..

[7]  Stephen P. Boyd,et al.  Efficient distance computation using best ellipsoid fit , 1992, Proceedings of the 1992 IEEE International Symposium on Intelligent Control.

[8]  Elmer G. Gilbert,et al.  Distance functions and their application to robot path planning in the presence of obstacles , 1985, IEEE J. Robotics Autom..

[9]  Elmer G. Gilbert,et al.  Computing the distance between general convex objects in three-dimensional space , 1990, IEEE Trans. Robotics Autom..

[10]  S. Sathiya Keerthi,et al.  Computation of certain measures of proximity between convex polytopes: a complexity viewpoint , 1992, Proceedings 1992 IEEE International Conference on Robotics and Automation.

[11]  Herbert Edelsbrunner,et al.  Computing the Extreme Distances Between Two Convex Polygons , 1985, J. Algorithms.

[12]  S. Sathiya Keerthi,et al.  A fast procedure for computing the distance between complex objects in three-dimensional space , 1988, IEEE J. Robotics Autom..