Task oriented area partitioning and allocation for optimal operation of multiple industrial robots in unstructured environments

© 2014 IEEE. When multiple industrial robots are deployed in field applications such as grit blasting and spray painting of steel bridges, the environments are unstructured for robot operation and the robot positions may not be arranged accurately. Coordination of these multiple robots to maximize productivity through area partitioning and allocation is crucial. This paper presents a novel approach to area partitioning and allocation by utilizing multiobjective optimization and voronoi partitioning. Multiobjective optimization is used to minimize: (1) completion time, (2) proximity of the allocated area to the robot, and (3) the torque experienced by each joint of the robot during task execution. Seed points of the voronoi graph for voronoi partitioning are designed to be the design variables of the multiobjective optimization algorithm. Results of three different simulation scenarios are presented to demonstrate the effectiveness of the proposed approach and the advantage of incorporating robots' torque capacity

Numerical modelling of ellipsoidal inclusions

Within the framework of numerical algorithms for the threedimensional random packing of granular materials this work presents an innovative formulation for polydispersed ellipsoidal particles, including an overlapping detection algorithm for an optimized simulation of the mesostructure of geomaterials, particularly concrete. Granular composite cement-based materials can be so reconstructed with adequate precision in terms of grain size distribution. Specifically, the algorithm performance towards the assumed inclusion shape (ellipsoidal or spheric) and degree of regularity (round or irregular) is here discussed. Examples on real grading curves prove that this approach is effective. The advantages of the proposed method for computational mechanics purposes are also disclosed when properly interfaced with visualization CAD (Computer Aided Design) tools

Comparaison de méthodes de détection automatique d’intersections sur surfaces paramétriques

La question de déterminer si un modèle géométrique a des intersections non prévues est commune à plusieurs domaines : simulations numériques, CAO/DAO, animation, infographie, etc. C’est un problème dont la complexité varie avec la représentation choisie pour créer le modèle. Pour les surfaces paramétriques c’est un problème difficile à résoudre, mais pour lequel plusieurs solutions ont été proposées. Ces solutions diffèrent les unes des autres dans leurs angles d’approche, leur complexité et la justesse de leurs résultats. Dans ce mémoire, nous tenterons de comparer certaines de ces méthodes. Nous nous concentrerons sur les méthodes dites failsafe, c’est-à-dire qui permettent assurément de détecter la possibilité d’une intersection s’il y en a une. Ces méthodes sont celles utilisées pour toutes les applications critiques, donc pour lesquelles un modèle mal formé aurait des conséquences importantes. Ce mémoire est à teneur principalement théorique. Nous comparerons les méthodes, dans un premier temps, sur leur puissance de résolution. Nous discuterons, dans un deuxième temps, de coût calculatoire. Nous avons finalement fait quelques implémenta- tions pour appuyer nos observations théoriques, mais nous n’avons pas fait une analyse empirique approfondie des coûts calculatoires. Ceci reste à faire. Nous verrons entre autre qu’il existe un ordre partiel entre certaines des méthodes, mais pas toutes. Par exemple, la méthode test-point est strictement plus puissante que la séparation des enveloppes convexes, mais elle est ni plus ni moins puissante que la méthode Volino-Thalmann.The question of determining if a given geometric model has extraneous intersections is common to many domains: numerical simulation, CAD/CAM, animation, computer graphics, etc. The complexity of this problem varies with the representation chosen to generate the model. For parametric surfaces, it is a hard problem, but for which many solutions have been proposed. Those solutions differ from one another by their underlying ideas, their complexity and the exactitude of the result they give. In this thesis, we will try to compare some of these methods. We will concentrate on the class of methods we call failsafe, the methods that will surely detect the possibility of an intersection if there is one. Those are the methods used in all critical applications, the applications in which a malformed model would have important consequences. The work of this thesis is mostly theoretical. First, we will compare the different techniques on their power of resolution. Then, we will discuss the execution cost of the different methods. We did some implementations while working on this thesis, but only as a way to support our theoretical observations. A complete empirical study of the execution times of the different methods would be left to do. We will see that there is a partial order between some of the methods in their strength, but not all of them. For example, the test-point method is strictly stronger than the separation of the convex hulls method, but is neither stronger nor weaker than the Volino- Thalmann method

Probabilistic Feature-Based Registration for Interventional Medicine

The need to compute accurate spatial alignment between multiple representations of patient anatomy is a problem that is fundamental to many applications in computer-integrated interventional medicine. One class of methods for computing such alignments is feature-based registration, which aligns geometric information of the shapes being registered, such as salient landmarks or models of shape surfaces. A popular algorithm for surface-based registration is the Iterative Closest Point (ICP) algorithm, which treats one shape as a cloud of points that is registered to a second shape by iterating between point-correspondence and point-registration phases until convergence. In this dissertation, a class of "most likely point" variants on the ICP algorithm is developed that offers several advantages over ICP, such as high registration accuracy and the ability to confidently assess the quality of a registration outcome. The proposed algorithms are based on a probabilistic interpretation of the registration problem, wherein the point-correspondence and point-registration phases optimize the probability of shape alignment based on feature uncertainty models rather than minimizing the Euclidean distance between the shapes as in ICP. This probabilistic framework is used to model anisotropic errors in the shape measurements and to provide a natural context for incorporating oriented-point data into the registration problem, such as shape surface normals. The proposed algorithms are evaluated through a range of simulation-, phantom-, and clinical-based studies, which demonstrate significant improvement in registration outcomes relative to ICP and state-of-the-art methods