Abstract
This paper presents development and control of a disc-typed one-wheel mobile robot, called GYROBO. Several models of the one-wheel mobile robot are designed, developed, and controlled. The current version of GYROBO is successfully balanced and controlled to follow the straight line. GYROBO has three actuators to balance and move. Two actuators are used for balancing control by virtue of gyro effect and one actuator for driving movements. Since the space is limited and weight balance is an important factor for the successful balancing control, careful mechanical design is considered. To compensate for uncertainties in robot dynamics, a neural network is added to the nonmodel-based PD-controlled system. The reference compensation technique (RCT) is used for the neural network controller to help GYROBO to improve balancing and tracking performances. Experimental studies of a self-balancing task and a line tracking task are conducted to demonstrate the control performances of GYROBO.
Highlights
Mobile robots are considered as quite a useful robot system for conducting conveying objects, conducting surveillance, and carrying objects to the desired destination
The balancing mechanism becomes an important issue in the mobile robot research
This paper is focused on the implementation and control of GYROBO rather than analyzing the dynamics model of the system since the modeling has been well presented in the literature [11]
Summary
Mobile robots are considered as quite a useful robot system for conducting conveying objects, conducting surveillance, and carrying objects to the desired destination. Most of mobile robots have a two-actuated wheel structure with three- or four-point contact on the ground to maintain stable pose on the plane. In research on balancing robots, two-point contact mobile robots are designed and controlled [1,2,3,4,5]. Successful balancing control of the spherical-wheel robot has been demonstrated. A one-wheel robot called GYROBO is designed, implemented, and controlled. Aforementioned research results provide β dynamics models for the one-wheel robot, in real physical βsystem, it is quite difficult to control GYROBO based on dynamic models due to several reasons. After careful design of the system, a neural network control scheme is applied to the model-free-controlled system to improve balancing performance achieved by linear controllers.
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