Intelligent control of a mobile robot using sliding modes, artificial neural networks and reinforcement learning
Applied Computational Intelligence, Patrol Robots, Intelligent Robots, Intelligent Control, Reinforcement Learning
Research on intelligent and autonomous mobile robots has grown significantly due to its military, civil and industrial applications, such as the monitoring of agricultural plantations, the use in actions to support environmental disasters, border patrol, mapping of submarine territories or even the study of animal behavior. This work rescues the multi and interdisciplinary motivation of artificial intelligence, starting from philosophical questions to reach the characterization of intelligent and autonomous systems. Thus, only after building the theoretical bases for the concept of these agents, a bioinspired approach
is presented for the trajectory tracking task by a mobile robot. For this purpose, the strategy consists of robust non-linear intelligent control using Sliding Modes, artificial neural networks and the Upper Confidence Bound algorithm. The characteristics of each technique give the robot robustness in the control task, learning and autonomy with decision-making, respectively. Thus, the algorithm developed for the trajectory tracking problem was designed based on the most recent arguments about autonomous agents and represents the growing trend of research in embodied cognitive science.