A model-predictive motion planner for the IARA autonomous car

• Ano de defesa: 2017
• Autor(a) principal: Cardoso, Vinícius Brito
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: eng
• Instituição de defesa: Universidade Federal do Espírito Santo; BR; Mestrado em Informática; Centro Tecnológico; UFES; Programa de Pós-Graduação em Informática
• Palavras-chave em Português: Planejamento de trajetória; Veículos autônomos; Robótica; Controle preditivo; Navegação de robôs móveis; Movimento - Planejamento; Ciência da Computação; 004
• Link de acesso: http://repositorio.ufes.br/handle/10/9855

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, IARAs 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).