Trabalho de Conclusão de Curso de Graduação
Redes neurais artificiais na otimização da computação de caminhos seguros em terrenos com topografia
Fecha
2022-08-10Autor
Weber, Crístian Marcos
Institución
Resumen
The computation of shortest paths (pathfinding) optimized for the topographical features of real
world terrains has been investigated in different problems in the areas of robotics, computer
games and simulation. Pathfinding for topographical realistic terrain models involves finding a
least-cost path evaluated in terms of distance traveled according to the slopes of the terrain. In
path computations performed to solve tactical and strategic problems, the pathfinding of safe
paths and with lower topographical costs is a subject still little explored in the literature. Among
other aspects, path safety may be related to the visual concealment of an agent traveling along
the path in relation to an observer located at a position on the terrain. The objective of this
work is to investigate how the computations of a heuristic pathfinding algorithm A∗ should be
performed so that an agent can use a path that allows the agent’s stealth locomotion in relation
to an observer. This indicates that the terrain relief features are important both for obtaining a
low-cost path and for computing the observer’s field of view and the consequent agent stealth
according to the terrain relief. For the use of heuristic pathfinding algorithms, determining an
efficient heuristic in terms of execution time and node expansion is a challenging task in this
secure topographic pathfinding problem. The proposal of this work, therefore, also involves
using and evaluating the learning capacity of a deep neural network in the approximation of the
heuristic to be used by the A∗ algorithm, thus allowing to optimize the computations of safe
routes with lower costs.