Resumen
The article focuses on research related to probabilistic robotics. The author says that planning and navigation algorithms exploit statistics gleaned from uncertain, imperfect real-world environments to guide robots toward their goals and around obstacles. In the past decade, the field of robotics has made notable progress in terms of flexibility autonomy, and human interaction. The probabilistic approach addresses them through a single idea as how to represent information probabilistically. The move to probabilistic techniques in robotics is paralleled in many other subfields of artificial intelligence, including computer vision, natural language understanding, and speech recognition. Classical robotics textbooks usually describe at length the kinematics and dynamics of robotic devices, addressing the question of how controls affect the state of the robot and, more broadly, the world. In high-dimensional state spaces, computational considerations may pose serious obstacles when estimating state. The ultimate goal of any robotics software system is to control robotic devices.