A constraint-stabilized time-stepping approach for piecewise smooth multibody dynamics

Rigid multibody dynamics is an important area of mathematical modeling which attempts to predict the position and velocity of a system of rigid bodies. Many methods will use smooth bodies without friction. The task is made especially more difficult in the face of noninterpenetration constraints, joint constraints, and friction forces. The difficulty that arises when noninterpenetration constraints are enforced is directly related to the fact that the usual methods of computing the distance between bodies do not give any indication of the amount of penetration when two bodies interpenetrate. Because we wish to calculate vectors that are normal to contact, and because it is necessary to determine the amount of penetration, when it exists, the classical computation of the depth of penetration when applied to convex polyhedral bodies is inefficient.We hereby describe a new method of determining when two convex polyhedra intersect and of evaluating a measure of the amount of penetration, when it exists. Our method is much more efficient than the classic computation of the penetration depth since it can be shown that its complexity grows only linearly with the size of the problem. We use our method to construct a signed distance function and implement it for use with a method for achieving geometrical constraint stabilization for a linear-complementarity-based time-stepping scheme for rigid multibody dynamics with joints, contact, and friction which, before now, was not equipped to handle polyhedral bodies. During our analysis, we describe how to compute normal vectors at contact, despite the cases when the classic derivative fails to exist.We put this analysis into a time-stepping procedure that uses a convex relaxation of a mixed linear complementarity problem with a resulting fixed point iteration that is guaranteed to converge if the friction is not too large, the time step is not too large, and the initial solution is feasible. Finally, we construct an algorithm that achieves constraint stabilization with quadratic convergence.The numerical results proved to be quite satisfactory, implying that the constraint stabilization holds, and that quadratic convergence exists

Minkowski Sum Construction and other Applications of Arrangements of Geodesic Arcs on the Sphere

We present two exact implementations of efficient output-sensitive algorithms that compute Minkowski sums of two convex polyhedra in 3D. We do not assume general position. Namely, we handle degenerate input, and produce exact results. We provide a tight bound on the exact maximum complexity of Minkowski sums of polytopes in 3D in terms of the number of facets of the summand polytopes. The algorithms employ variants of a data structure that represents arrangements embedded on two-dimensional parametric surfaces in 3D, and they make use of many operations applied to arrangements in these representations. We have developed software components that support the arrangement data-structure variants and the operations applied to them. These software components are generic, as they can be instantiated with any number type. However, our algorithms require only (exact) rational arithmetic. These software components together with exact rational-arithmetic enable a robust, efficient, and elegant implementation of the Minkowski-sum constructions and the related applications. These software components are provided through a package of the Computational Geometry Algorithm Library (CGAL) called Arrangement_on_surface_2. We also present exact implementations of other applications that exploit arrangements of arcs of great circles embedded on the sphere. We use them as basic blocks in an exact implementation of an efficient algorithm that partitions an assembly of polyhedra in 3D with two hands using infinite translations. This application distinctly shows the importance of exact computation, as imprecise computation might result with dismissal of valid partitioning-motions.Comment: A Ph.D. thesis carried out at the Tel-Aviv university. 134 pages long. The advisor was Prof. Dan Halperi

Efficient Configuration Space Construction and Optimization for Motion Planning

The configuration space is a fundamental concept that is widely used in algorithmic robotics. Many applications in robotics, computer-aided design, and related areas can be reduced to computational problems in terms of configuration spaces. In this paper, we survey some of our recent work on solving two important challenges related to configuration spaces

Efficient configuration space construction and optimization

The configuration space is a fundamental concept that is widely used in algorithmic robotics. Many applications in robotics, computer-aided design, and related areas can be reduced to computational problems in terms of configuration spaces. In this dissertation, we address three main computational challenges related to configuration spaces: 1) how to efficiently compute an approximate representation of high-dimensional configuration spaces; 2) how to efficiently perform geometric, proximity, and motion planning queries in high dimensional configuration spaces; and 3) how to model uncertainty in configuration spaces represented by noisy sensor data. We present new configuration space construction algorithms based on machine learning and geometric approximation techniques. These algorithms perform collision queries on many configuration samples. The collision query results are used to compute an approximate representation for the configuration space, which quickly converges to the exact configuration space. We highlight the efficiency of our algorithms for penetration depth computation and instance-based motion planning. We also present parallel GPU-based algorithms to accelerate the performance of optimization and search computations in configuration spaces. In particular, we design efficient GPU-based parallel k-nearest neighbor and parallel collision detection algorithms and use these algorithms to accelerate motion planning. In order to extend configuration space algorithms to handle noisy sensor data arising from real-world robotics applications, we model the uncertainty in the configuration space by formulating the collision probabilities for noisy data. We use these algorithms to perform reliable motion planning for the PR2 robot.Doctor of Philosoph