Algorithms for detecting dependencies and rigid subsystems for CAD

Geometric constraint systems underly popular Computer Aided Design soft- ware. Automated approaches for detecting dependencies in a design are critical for developing robust solvers and providing informative user feedback, and we provide algorithms for two types of dependencies. First, we give a pebble game algorithm for detecting generic dependencies. Then, we focus on identifying the "special positions" of a design in which generically independent constraints become dependent. We present combinatorial algorithms for identifying subgraphs associated to factors of a particular polynomial, whose vanishing indicates a special position and resulting dependency. Further factoring in the Grassmann- Cayley algebra may allow a geometric interpretation giving conditions (e.g., "these two lines being parallel cause a dependency") determining the special position.Comment: 37 pages, 14 figures (v2 is an expanded version of an AGD'14 abstract based on v1

Geometric Over-Constraints Detection: A Survey

Currently, geometric over-constraints detection is of major interest in several diferent felds. In terms of product development process (PDP), many approaches exist to compare and detect geometric over-constraints, to decompose geometric systems, to solve geometric constraints systems. However, most approaches do not take into account the key characteristics of a geometric system, such as types of geometries, diferent levels at which a system can be decomposed e.g numerical or structural. For these reasons, geometric over-constraints detection still faces challenges to fully satisfy real needs of engineers. The aim of this paper is to review the state-of-the-art of works involving with geometric over-constraints detection and to identify pos sible research directions. Firstly, the paper highlights the user requirements for over-constraints detection when modeling geometric constraints systems in PDP and proposes a set of criteria to analyze the available methods classifed into four categories: level of detecting over-constraints, system decomposition, system modeling and results generation. Secondly, it introduces and analyzes the available methods by grouping them based on the introduced criteria. Finally, it discusses pos sible directions and future challenges

Re-parameterization reduces irreducible geometric constraint systems

International audienceYou recklessly told your boss that solving a non-linear system of size n (n unknowns and n equations) requires a time proportional to n, as you were not very attentive during algorithmic complexity lectures. So now, you have only one night to solve a problem of big size (e.g., 1000 equations/unknowns), otherwise you will be fired in the next morning. The system is well-constrained and structurally irreducible: it does not contain any strictly smaller well-constrained subsystems. Its size is big, so the Newton–Raphson method is too slow and impractical. The most frustrating thing is that if you knew the values of a small number k<<n of key unknowns, then the system would be reducible to small square subsystems and easily solved. You wonder if it would be possible to exploit this reducibility, even without knowing the values of these few key unknowns. This article shows that it is indeed possible. This is done at the lowest level, at the linear algebra routines level, so that numerous solvers (Newton–Raphson, homotopy, and also p-adic methods relying on Hensel lifting) widely involved in geometric constraint solving and CAD applications can benefit from this decomposition with minor modifications. For instance, with k<<n key unknowns, the cost of a Newton iteration becomes O(kn^2) instead of O(n^3). Several experiments showing a significant performance gain of our re-parameterization technique are reported in this paper to consolidate our theoretical findings and to motivate its practical usage for bigger systems

On the detection of over-constrained subparts of configurations when deforming free-form curves

Today, designers use CAD modelers to define and modify NURBS surfaces involved in the design of complex shapes like car bodies or turbine blades. The generated shapes often result from the use of variational modeling techniques where user-specified constraints define the shapes. However, for free-form curve/surfaces, if too much constraints are added to subparts of a configuration, the system will not be solvable even if it is globally well-/under-constrained. When this happens, it is useful to identify locally unsatisfiable subparts of configurations and provide the user feedback for adjustment. Currently, in the domain of geometric constraint solving, techniques are mainly developed for Euler geometries rather than parametric entities like free-form curves/surfaces. In this paper, we apply the Dulmage-Mendelsohn decomposition method to isolate structural over-constrained subparts of configurations. Since structural over-constraints do not necessarily mean unsatisfiable, a Jacobian matrix analysis approach is taken to further detect the inconsistent constraints. Indeed, these numerical methods can be generalized to detect overconstraints on free-form curves. We illustrate our approach on different examples where results show that Gauss elimination, though restricted to linear cases, is more relevant in our context than Dulmage-Mendelsohn decomposition

Towards a better integration of modelers and black box constraint solvers within the Product Design Process

This paper presents a new way of interaction between modelers and solvers to support the Product Development Process (PDP). The proposed approach extends the functionalities and the power of the solvers by taking into account procedural constraints. A procedural constraint requires calling a procedure or a function of the modeler. This procedure performs a series of actions and geometric computations in a certain order. The modeler calls the solver for solving a main problem, the solver calls the modeler’s procedures, and similarly procedures of the modeler can call the solver for solving sub-problems. The features, specificities, advantages and drawbacks of the proposed approach are presented and discussed. Several examples are also provided to illustrate this approach

Over-constraints detection and resolution in geometric equation systems

This paper proposes an original decision-support approach to address over-constrained geometric configurations in Computer-Aided Design. It focuses particularly on the detection and resolution of redundant and conflicting constraints when deforming free-form surfaces made of NURBS patches. Based on a series of structural decompositions coupled with numerical analyses, the proposed approach handles both linear and non-linear constraints. The structural decompositions are particularly efficient because of the local support property of NURBS. Since the result of this detection process is not unique, several criteria are introduced to drive the designer in identifying which constraints should be removed to minimize the impact on his/her original design intent. Thus, even if the kernel of the algorithm works on equations and variables, the decision is taken by considering the user-specified geometric constraints. The method is illustrated on academic and industrial examples realized with our prototype software

Spacecraft high-voltage power supply construction

The design techniques, circuit components, fabrication techniques, and past experience used in successful high-voltage power supplies for spacecraft flight systems are described. A discussion of the basic physics of electrical discharges in gases is included and a design rationale for the prevention of electrical discharges is provided. Also included are typical examples of proven spacecraft high-voltage power supplies with typical specifications for design, fabrication, and testing