Abstract
Stabilization of a single wheel mobile robot attracted researcher attentions in robotic area. However, the budget requirements for building experimental setups capable in investigating isolated parameters and implementing others encouraged the development of new simulation methods and techniques that beat such limitations. In this work we have developed a simulation platform for testing different control tactics to stabilize a single wheel mobile robot. The graphic representation of the robot, the dynamic solution, and, the control scheme are all integrated on common computer platform using Visual Basic. Simulation indicates that we can control such robot without knowing the detail of it's internal structure or dynamics behaviour just by looking at it and using manual operation tactics. Twenty five rules are extracted and implemented using Takagi-Sugeno's fuzzy controller with significant achievement in controlling robot motion during the dynamic simulation. The resulted data from the successful implementation of the fuzzy model are used to utilize and train a neurofuzzy controller using ANFIS scheme to produce further improvement in robot performance
Highlights
Self-stabilization of a single rolling wheel using a gyroscopic actuation was under several explorations for its importance in robotic applications
M. 2005) developed methodolgy to control the longitudinal motion of a single wheel robot on an uneven surface
The present work is targeting simulation tools and techniques that might result in lowering such price tag by using virtual prototyping and real time simulation in controlling such system under different manoeuvring tasks using intelligent control scheme
Summary
Self-stabilization of a single rolling wheel using a gyroscopic actuation was under several explorations for its importance in robotic applications The mechanical design consists basically of a gyro disk attached to internally suspended pendulum. Such arrangement provides a forward and reverse movement in which the reaction of the applied motor torque is counteracted by the moment of the hanging mass of the gyroscope and gimbals system as shown in Figures 1 and 2. The platform gained feedback from the environment using a tilt sensor and electronic compass for both balancing and heading It included speed detection and object avoidance by using sonar sensor and shaft encoder on the main drive motor. It is demonstrated experimentally that the wheel can automatically be controlled by using the learned human control input Using the neural networks and fuzzy logic in the controller design via neurofuzzy would provide both learning and response readability
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