Graphics-assisted approach to rapid collision detection for multi-axis machining

Collision detection is a critical problem in multi-axis NC machining with complex machining environments. Using a combination of machining environment culling and a two-phase collision detection strategy, a rapid and robust collision detection approach is implemented. With this approach, the machining environment is initially subdivided and organized hierarchically using a binary space partitioning (BSP) tree structure. Before carrying out precise collision detection, the machining environment is culled conservatively to remove most of the irrelevant geometries from further checking steps which are relatively time consuming. For the later stage of precise collision detection, which is the most complicated and time-consuming task in many existing approaches, a strategy of using two-phase collision detection is developed in order to make the approach not only efficient in computation and but also maintain a high detection precision at the same time. Another important benefit of this approach is that it takes advantage of the rapid performance of graphics hardware to generate visibility information very efficiently. We show how this information could be used in assisting collision detection. An example of five-axis machining in a complex environment is adopted as the case study to demonstrate the proposed approach. It has shown that the collision detection task can be achieved with a near real-time performance.