A Model-Predictive Motion Planner for the IARA autonomous car
| Autor: | Vinicius B. Cardoso, Lucas de Paula Veronese, Thomas Teixeira, Alberto F. De Souza, Josias Oliveira, Claudine Badue, Filipe Mutz, Thiago Oliveira-Santos |
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| Rok vydání: | 2017 |
| Předmět: |
FOS: Computer and information sciences
050210 logistics & transportation 0209 industrial biotechnology Computer science 05 social sciences I.2.9 02 engineering and technology Planner Motion (physics) Computer Science - Robotics 020901 industrial engineering & automation Position (vector) Control theory 0502 economics and business Path (graph theory) Trajectory Robotics (cs.RO) computer computer.programming_language |
| Zdroj: | ICRA |
| DOI: | 10.1109/icra.2017.7989028 |
| Popis: | 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 eventual 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). Comment: This is a preprint. Accepted by 2017 IEEE International Conference on Robotics and Automation (ICRA) |
| Databáze: | OpenAIRE |
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