Design of a mobile robot’s control system for obstacle identification and avoidance using sensor fusion and model predictive control
The aim of this master thesis is to design a control system based on model predictive control (MPC) with sensor data fusion for obstacle avoidance. Since the amount of obtained data is larger due to multiple sensors, the required sampling time has to be larger enough in comparison with the calcul...
| Autor Principal: | Barreto Guerra, Jean Paul |
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| Formato: | Tesis de Maestría |
| Idioma: | Inglés |
| Publicado: |
Pontificia Universidad Católica del Perú
2017
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| Materias: | |
| Acceso en línea: |
http://tesis.pucp.edu.pe/repositorio/handle/123456789/9507 |
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| Sumario: |
The aim of this master thesis is to design a control system based on model predictive
control (MPC) with sensor data fusion for obstacle avoidance. Since the amount of
obtained data is larger due to multiple sensors, the required sampling time has to be
larger enough in comparison with the calculation time of the optimal problem. Then
it is proposed a simplification of the mobile robot model in order to reduce this optimization
time.
The sensor data fusion technique uses the range information of a laser scanner and
the data of a mono-camera acquired from image processing techniques. In image processing
different detection algorithms are proposed such as shape and color detection.
Therefore an estimation of the obstacles dimension and distance is explained obtaining
accurate results.
Finally a data fusion for obstacle determination is developed in order to use this
information in the optimization control problem as a path constraint. The obtained
results show the mobile robot behavior in trajectories tracking and obstacle avoidance
problems by comparing two different sampling times. It is concluded that the mobile
robot reaches the final desired position while avoiding the detected obstacles along the
trajectory. |
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