Faster parallel collision detection at high resolution for CNC milling applications

Abstract

This paper presents a new and more work-efficient parallel method to speed up a class of three-dimensional collision detection (CD) problems, which arise, for instance, in computer numerical control (CNC) milling. Given two objects, one enclosed by a bounding volume and the other represented by a voxel model, we wish to determine all possible orientations of the bounded object around a given point that do not cause collisions. Underlying most CD methods are 3 types of geometrical operations that are bottlenecks: decompositions, rotations, and projections. Our proposed approach, which we call the aggressive inaccessible cone angle (AICA) method, simplifies these operations and, empirically, can prune as much as 99% of the intersection tests that would otherwise be required and improve load balance. We validate our techniques by implementing a parallel version of AICA in SculptPrint, a state-of-the-art computer-aided manufacturing (CAM) application used CNC milling, for GPU platforms. Experimental results using 4 CAM benchmarks show that AICA can be over 23× faster than a baseline method that does not prune projections, and can check collisions for 4096 angle orientations in an object represented by 27 million voxels in less than 18 milliseconds on a GPU.