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Integration of Cell-Mapping and Reinforcement-Learning Techniques for Motion Planning of Car-Like Robots

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4 Author(s)
Mariano Gomez Plaza ; Dept. de Autom., Univ. de Alcala, Alcala de Henares, Spain ; TomÁs Martinez-Marin ; SebastiÁn Sanchez Prieto ; Daniel Meziat Luna

The aim of this work has been to integrate the Cartesian space together with the kinematics and dynamics spaces of a car-like robot. We propose a new algorithm that obtains a minimum-time solution to the optimal motion planning of the vehicle. The new algorithm is based on the combination of cell-mapping and reinforcement-learning techniques. This algorithm can obtain the environment and vehicle parameters from received experience without needing a mathematical model. The algorithm uses a transformation of the cell-to-cell transitions to reduce the time that is spent in the knowledge of the vehicle dynamics and environment. Four state variables have been considered: 1) the velocity of the vehicle; 2) the x Cartesian coordinate; 3) the y Cartesian coordinate; and 4) the orientation of the vehicle. In addition, two different control actions can act on the vehicle: 1) the traction torque that was used for speeding up/braking the vehicle and 2) the steering angle. The results show the applicability of the proposed algorithm in environments with the presence of obstacles.

Published in:

IEEE Transactions on Instrumentation and Measurement  (Volume:58 ,  Issue: 9 )