Simulação computacional de medidas estereológicas em estruturas de metal duro (WC-Co)

This work focuses on the creation and applications of a dynamic simulation software in order to study the hard metal structure (WC-Co). The technological ground used to increase the GPU hardware capacity was Geforce 9600 GT along with the PhysX chip created to make games more realistic. The software simulates the three-dimensional carbide structure to the shape of a cubic box where tungsten carbide (WC) are modeled as triangular prisms and truncated triangular prisms. The program was proven effective regarding checking testes, ranging from calculations of parameter measures such as the capacity to increase the number of particles simulated dynamically. It was possible to make an investigation of both the mean parameters and distributions stereological parameters used to characterize the carbide structure through cutting plans. Grounded on the cutting plans concerning the analyzed structures, we have investigated the linear intercepts, the intercepts to the area, and the perimeter section of the intercepted grains as well as the binder phase to the structure by calculating the mean value and distribution of the free path. As literature shows almost consensually that the distribution of the linear intercepts is lognormal, this suggests that the grain distribution is also lognormal. Thus, a routine was developed regarding the program which made possible a more detailed research on this issue. We have observed that it is possible, under certain values for the parameters which define the shape and size of the Prismatic grain to find out the distribution to the linear intercepts that approach the lognormal shape. Regarding a number of developed simulations, we have observed that the distribution curves of the linear and area intercepts as well as the perimeter section are consistent with studies on static computer simulation to these parameters.

Mapeamento robótico 2,5-D com representação em grade de ocupação-elevação

This work introduces a new method for environment mapping with three-dimensional information from visual information for robotic accurate navigation. Many approaches of 3D mapping using occupancy grid typically requires high computacional effort to both build and store the map. We introduce an 2.5-D occupancy-elevation grid mapping, which is a discrete mapping approach, where each cell stores the occupancy probability, the height of the terrain at current place in the environment and the variance of this height. This 2.5-dimensional representation allows that a mobile robot to know whether a place in the environment is occupied by an obstacle and the height of this obstacle, thus, it can decide if is possible to traverse the obstacle. Sensorial informations necessary to construct the map is provided by a stereo vision system, which has been modeled with a robust probabilistic approach, considering the noise present in the stereo processing. The resulting maps favors the execution of tasks like decision making in the autonomous navigation, exploration, localization and path planning. Experiments carried out with a real mobile robots demonstrates that this proposed approach yields useful maps for robot autonomous navigation