Motion Planning of a Non-holonomic Vehicle in a Real Environment by Reinforcement Learning*

Gomez, M.; Gayarre, L.; Martínez-Marín, T.; Sánchez, S. F.; Meziat, D.

doi:10.1007/978-3-642-02478-8_102

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2011

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“…In ref. [30], the algorithm is tested under semi-real conditions because it was not embedded in the vehicle but it was running in a computer, which kept a Bluetooth link with the vehicle in order to send both its localisation and the new control action. Due to good behaviour under this scenario, the next step is to give the vehicle full autonomy for the execution of the algorithm and to test it taking into account different goal states and therefore different learning stages.…”

Section: Introductionmentioning

confidence: 99%

Optimal motion planning by reinforcement learning in autonomous mobile vehicles

et al. 2011

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SUMMARYThe aim of this work has been the implementation and testing in real conditions of a new algorithm based on the cell-mapping techniques and reinforcement learning methods to obtain the optimal motion planning of a vehicle considering kinematics, dynamics and obstacle constraints. The algorithm is an extension of the control adjoining cell mapping technique for learning the dynamics of the vehicle instead of using its analytical state equations. It uses a transformation of cell-to-cell mapping in order to reduce the time spent during the learning stage. Real experimental results are reported to show the satisfactory performance of the algorithm.

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Section: Introductionmentioning

confidence: 99%