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.

This work aims to present a new optimal control scheme based on the CACM-RL technique applied tounstable systems such as a Two-Wheeled Inverted Pendulum (TWIP).The main challenge in this work is to verify and validate the good behaviour of CACM-RL in this kind of system. Learning while maintaining the equilibrium is a complex task. It is easy in stable platforms because the system never reaches an unstable state, but in unstable systems it is very difficult. The study also investigates implementing CACM-RL to coexist with a classic control solution. The results show that the proposed method works perfectly in unstable systems, providing better results than a PID controller.

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