Name: Michael Andre Gonçalves
Type: MSc dissertation
Publication date: 28/08/2013

Namesort descending Role
Alberto Ferreira De Souza Advisor *
Thiago Oliveira dos Santos Co-advisor *

Examining board:

Namesort descending Role
Alberto Ferreira De Souza Advisor *
Edgar Schneider Internal Examiner *
Edilson de Aguiar Internal Examiner *
Luiz Chaimowicz External Examiner *
Thiago Oliveira dos Santos Co advisor *

Summary: In this work, we investigated the use of A-star algorithms (A*) with hybrid state in au-tonomous navigation of vehicles in a three-dimensional space. We have modeled the vehicle position (origin), the goal and other points of interest in the world (states) as nodes of a graph. The cost of navigating between these nodes were modeled as edges of the graph, and a variant of the A* algorithm was used to choose the best path be-tween origin and goal. In order to be able to avoid obstacles and achieve fast algorithm, we used a combination of two heuristics to estimate the cost of the current node to the goal node: one considering only the obstacles and without the limitation of rotation of the vehicle, and its dual disregarding the obstacles and with limited cinematic R³.
We implemented the proposed navigation solution and incorporated it to the framework of robotics CARMEN as a navigation module for autonomous vehicles. Our module interacts with other existing modules (interface modules with sensors, mapping, local-ization, etc.) by means of message exchanging. It enables practical use of the algo-rithm. Results of experiments performed on IARA (Intelligent Robotic Autonomous Automobile - autonomous drive car developed in UFES) showed the viability of using the algorithm in simple and structured environments, such as roads, as well as in un-structured and complex environments, such as parking lots and unpaved areas.

Access to document

Acesso à informação
Transparência Pública

© 2013 Universidade Federal do Espírito Santo. Todos os direitos reservados.
Av. Fernando Ferrari, 514 - Goiabeiras, Vitória - ES | CEP 29075-910