A Model-Predictive Motion Planner for the IARA Autonomous Car

Vinicius Cardoso1, Josias Oliveira2, Thomas Teixeira2, Claudine Badue2, Filipe Wall Mutz2, Thiago Oliveira-Santos3, Lucas Veronese4, Alberto F. De Souza2

  • 1Universidade Federal do Espirito Santo
  • 2Universidade Federal do Espírito Santo
  • 3Universidade Federal do Espirito Santo - UFES
  • 4Universidad Técnica Federico Santa María

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Regular Papers

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09:55 - 11:10 | Tue 30 May | Room 4411/4412 | TUA4

ITS perception & planning

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Abstract

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 of occasional obstacles. Our experiments have shown that MPMP is able to compute trajectories that precisely follow a path produced by a Human driver (distance of 0.15 m in average) while smoothly driving IARA at speeds of up to 32.4 km/h (9 m/s).

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