Mobile robot should be able to perceive changes in the surrounding environment and in accordance with changes in the\nenvironment appropriate to adjust their action path and behavioral strategies [1]. In the field of military, mobile robot technology\nhas been applied to a variety of advanced unmanned early warning aircraft, demining robots; In the civil field, domestic mobile,\nentertainment, medical and other types of mobile robots more and more people in the field of vision. In short, the mobile robot has\na very broad space for development and application prospects. However, navigation is a necessary problem to be solved by the\nmobile robot, which determines the action set of the mobile robot from the initial point to the target point, and avoids the collision\nwith the obstacle [2,3]. The existing algorithms include grid method, potential force method and fuzzy control method. These\nalgorithms must be designed by the professionals according to the surrounding environment of the robot, and the environment\nchanges will affect the navigation and obstacle avoidance of the mobile robot. And even the need to rewrite the control procedures\nby experts, bringing expensive human and material resources [4,5]. Aiming at the existing navigation algorithms of mobile robots,\nA navigation controller for mobile robot based on batch demonstration learning is proposed. According to the frame of\ndemonstration and the actual situation of the mobile robot, a mobile robot model based on demonstration learning is designed. And\nthe neural network learning algorithm is used to compensate the non-linear term between the environment state and the action in\nthe model. Using the control method proposed in this paper, a two-wheeled mobile robot is used to simulate an arbitrary path in an\nobstacle-free environment in order to realize autonomous navigation.
Loading....