Abstract
Abstract Precise motion control is a challenging and important goal in the application of mobile robots. The mechanical structure of a novel mobile robot is presented. Using the support vector machine learning control method in statistical theory, the human control strategy is represented by the parametric model without knowledge of the actual robot mathematical model. Moreover, using the learning controller, the position motion control experiments of the robot are carried out. The results of the experiments show that this learning control method is feasible and valid for the precise position control of the mobile robot, and the maxim error is less than 32 cm in a 10 m linear movement.
Highlights
The control system and mechanical parts of spherical mobile robots are encapsulated in a round shell
The learning control method in this paper is to model the human operator control process
The human control strategy can be mapped between the states of the robot and the operator input commands: First, the human control output data and current states of robot is gathered; second, the support vector machine (SVM) learning method is used to model the human control operating and the human control parameters are stored for the task; third, the learning controller is set up by the offline learning computing; and the learning controller is implemented on the central controller in the robot
Summary
The control system and mechanical parts of spherical mobile robots are encapsulated in a round shell. Using the outer round shell, spherical mobile robots can make themselves move around. The robot makes contact to the ground with the help of the round shell at a single point, and is driven by the inner actuation mechanism. The precise position control of these kinds of robots is a challenging problem for the application. Based on learning the human operator control process, an approach of position control for the spherical mobile robot is presented.
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