We consider the problem of creating tighter-fitting bounding volumes (more specifically rectangular swept spheres) when constructing bounding volume hierarchies (BVHs) for complex 3D geometries given in the form of unstructured triangle meshes/soups with the aim of speeding up our IPS Path Planner for rigid bodies, where the triangles often have very different sizes. Currently, the underlying collision and distance computation module (IPS CDC) does not take into account the sizes of the triangles when it constructs BVHs using a top-down strategy. To split triangles in a BVH node into two BVH nodes, IPS CDC has to compute both the split axis and the split position. In this work, we use the principal axes of the tensor of inertia as the potential split axes and the center of mass as the split position, where the computations of both the tensor of inertia and the center of mass require knowledge of the areas of the triangles. We show that our method improves performance (up to 20 % faster) of our IPS Path Planner when it is used to plan collision-free disassembly paths for three different test cases taken from manufacturing industries.

Path planning of an object which is allowed to be in contact with other objects during assembly process is a significant challenge due to the variety of permitted or forbidden collisions between the distinct parts of the objects to be assembled. In order to put objects together in real-life scenarios, parts of assembled objects may be required to flex, whereas other parts may have to fit exactly. Consequently, existing collision checking and distance computation algorithms have to be modified to enable path planning of objects that can be in contact during the assembly process. In this paper, we analyze an improved broad phase proximity query algorithm to enable such contact-based assembly tasks we call CHAT (Contact-based Hierarchy for Assembly Tasks). We demonstrate that, compared to existing approaches, our proposed method is more than an order of magnitude faster for collision queries and up to three times faster for distance queries when the two objects contain a large number of parts (with some parts containing thousands or tens of thousands of triangles). Due to the nature of the algorithm, we expect the performance improvements to increase as the number of parts in an object becomes larger.

In this paper, algorithms have been developed that are capable of efficiently pre-processing massive point clouds for the rapid computation of the shortest distance between a point cloud and other objects (e.g. triangulated, point-based, etc.). This is achieved by exploiting fast distance computations between specially structured subsets of a simplified point cloud and the other object. This approach works for massive point clouds even with a small amount of RAM and was able to speed up the computations, on average, by almost two orders of magnitude. Given only 8 GB of RAM, this resulted in shortest distance computations of 30 frames per second for a point cloud originally having 1 billion points. The findings and implementations will have a direct impact for the many companies that want to perform path-planning applications through massive point clouds since the algorithms are able to produce real-time distance computations on a standard PC.

In this article we present a heuristic algorithm to compute the largest volume of an object in three dimensions that can move collision-free from a start configuration to a goal configuration through a virtual environment. The results presented here provide industrial designers with a framework to reduce the number of design iterations when designing parts to be placed in tight spaces.