Intelligent control of an omnidirectional mobile robot with reinforcement learning for decision making
Artificial neural networks, Epsilon greedy, Intelligent control, Mobile robot, Reinforcement learning
The evolution of robotic systems has become evident over time. Due to the advances in mechanical manufacturing and the new algorithms used, mobile robots have become increasingly independent in their actions. Regarding machine learning strategies, special attention is given to reinforcement learning algorithms, because of its similarities with the biological learning process. This work proposes the development of an autonomous agent, combining intelligent control strategies with decision-making algorithms. For the implementation of the proposed strategy, the Robotino omnidirectional mobile robot will be used. Simulations of the robot's performance were performed to explore space in an environment, for which a specific mathematical model is applied. For system control, the Linearization by Feedback strategy was combined with a compensator based on Artificial Neural Networks to deal with uncertainties and possible external disturbances. The epsilon greedy algorithm, in turn, was chosen to enable the robot in the decision-making process. The results show that the intelligent control strategy was efficient and the proposed intelligent agent was able to explore the environment effectively, obtaining a high average reward. The perspective is that the strategy is still implemented experimentally in Robotino.