Incremental penetration depth estimation between convex polytopes using dual-space expansion

Penetration Depth Between Two Convex Polyhedra: An Efficient Stochastic Global Optimization Approach

During the detailed design phase of an aerospace program, one of the most important consistency checks is to ensure that no two distinct objects occupy the same physical space. Since exact geometrical modeling is usually intractable, geometry models are discretized, which often introduces small interferences not present in the fully detailed model. In this paper, we focus on computing the depth of the interference, so that these false positive interferences can be removed, and attention can be properly focused on the actual design. Specifically, we focus on efficiently computing the penetration depth between two polyhedra, which is a well-studied problem in the computer graphics community. We formulate the problem as a constrained five-variable global optimization problem, and then derive an equivalent unconstrained, two-variable nonsmooth problem. To solve the optimization problem, we apply a popular stochastic multistart optimization algorithm in a novel way, which exploits the advantages of each problem formulation simultaneously. Numerical results for the algorithm, applied to 14 randomly generated pairs of penetrating polytopes, illustrate both the effectiveness and efficiency of the method.

A Review of Methods to Compute Minkowski Operations for Geometric Overlap Detection

This article provides an extensive review of algorithms for constructing Minkowski sums and differences of polygons and polyhedra, both convex and non-convex, commonly known as no-fit polygons and configuration space obstacles. The Minkowski difference is a set operation, which when applied to shapes defines a method for efficient overlap detection, providing an important tool in packing and motion-planning problems. This is the first complete review on this specific topic, and aims to unify algorithms spread over the literature of separate disciplines.