Fast Collision Detection Method Research Articles

A fast collision detection method based on point clouds and stretched primitives for manipulator obstacle-avoidance motion planning

A fast collision detection method based on point clouds and stretched primitives for manipulator obstacle-avoidance motion planning

Simulation-based fast collision detection for scaled polyhedral objects in motion by exploiting analytical contact equations

SUMMARYBased on the results of the study of convex object motion1 (J. Hopcroft and G. Wilfong, “Motion of objects in contact,” Int. J. Robot. Res., 4(4), 32–46 (1986)), this paper addresses the problem of exact collision detection of a pair of scaled convex polyhedra in relative motion, and determines the contact conditions of tangential contact features, arbitrary relative motion involving translation and rotation, and uniform scaling of the objects about a fixed point. We propose a new concept of the decision curve based on analytical contact equations that characterize a continuum of scaling factors (or a single scaling factor), which ensures that a pair of objects undergoing a scaling transformation will maintain the same tangential contact feature pair (or make instantaneous tangential contact feature transitions). We propose a reliable simulation-based approach to construct the decision curve by hybridizing analytical contact equations and conventional collision detection method, called the Fast Collision Detection Method (FCDM). This method can determine whether two scaled objects will make contact at specific tangential contact features (vertices, edges, or faces) under particular uniform scaling factors and after distinctive relative motion with better accuracy and less computational time than the existing collision detection methods. Finally, we demonstrate our approach for solving motion design in simple assembly/disassembly problems.

Fast collision detection method in virtual surgery

Fast collision detection method in virtual surgery

修正深度剥離を用いた高速衝突判定手法

We propose a fast collision detection method that uses graphics hardware and includes self-collision and self-collision and deformable objects as its targets. The method uses a layered depth image (LDI), which is generated by depth peeling, and transform feedback. We modify the depth peeling method so that it can peel object surfaces exactly and uses LDI representations for fast collision detection. In addition, transform feedback makes the collision detection process very fast since there is no need to read back from the GPU to the CPU. The proposed method was implemented in a PC having an NVIDIA GeForce 9800 GT graphics card and applied to two objects consisting of 10,000 triangles in total at an image-space resolution of 400 × 400 pixels. As a result, the collision detection time was about 24 ms with 96% detection.

Shape from silhouette using topology-adaptive mesh deformation

We present a computationally efficient and robust shape from silhouette method based on topology-adaptive mesh deformation, which can produce accurate, smooth, and topologically consistent 3D mesh models of complex real objects. The deformation scheme is based on the conventional snake model coupled with local mesh transform operations that control the resolution and uniformity of the deformable mesh. Based on minimum and maximum edge length constraints imposed on the mesh, we describe a fast collision detection method which is crucial for computational efficiency of the reconstruction process. The topology of the deformable mesh, which is initially zero genus, can be modified whenever necessary by merging operations in a controlled and robust manner by exploiting the topology information available in the silhouette images. The performance of the proposed shape from silhouette technique is demonstrated on several real objects.