A Model-Predictive Motion Planner for the IARA Autonomous Car

Nome: Vinícius Brito Cardoso
Tipo: Dissertação de mestrado acadêmico
Data de publicação: 23/11/2017
Orientador:

Nomeordem decrescente Papel
Alberto Ferreira De Souza Orientador
Claudine Santos Badue Gonçalves Co-orientador

Banca:

Nomeordem decrescente Papel
Alberto Ferreira De Souza Orientador
Claudine Santos Badue Gonçalves Coorientador
Fernando Santos Osório Examinador Externo
Thiago Oliveira dos Santos Examinador Interno

Resumo: In this work, we present the Model-Predictive Motion Planner (MPMP) of the Intelligent Autonomous Robotic Automobile (IARA). IARA is a fully autonomous car that uses a path planner to compute a path from its current position to the desired destination. Using this path, the current position, a goal in the path and a map, IARA’s MPMP is able to compute smooth trajectories from its current position to the goal in less than 50 ms. MPMP computes the poses of these trajectories so that they follow the path closely and, at the same time, are at a safe distance from occasional obstacles. Our experiments have shown that MPMP is able to compute trajectories that follow precisely a path produced by a human driver (distance of 0.15m in average) while smoothly driving IARA at speeds of up to 32.4 km/h (9 m/s).

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