Sensorimotor self-learning model based on operant conditioning for two-wheeled robot
- 52 Downloads
Traditional control methods of two-wheeled robot are usually model-based and require the robot’s precise mathematic model which is hard to get. A sensorimotor self-learning model named SMM TWR is presented in this paper to handle these problems. The model consists of seven elements: the discrete learning time set, the sensory state set, the motion set, the sensorimotor mapping, the state orientation unit, the learning mechanism and the model’s entropy. The learning mechanism for SMM TWR is designed based on the theory of operant conditioning (OC), and it adjusts the sensorimotor mapping at every learning step. This helps the robot to choose motions. The leaning direction of the mechanism is decided by the state orientation unit. Simulation results show that with the sensorimotor model designed, the robot is endowed the abilities of self-learning and self-organizing, and it can learn the skills to keep itself balance through interacting with the environment.
Key wordstwo-wheeled robot sensorimotor model self-learning operant conditioning (OC)
CLC numberTP 181
Unable to display preview. Download preview PDF.
Part of this research was done at the Department of Psychology, Michigan State University. The authors would like to express their thanks to Professor LIU Taosheng and his lab for help.
- SUPRAPTO B Y, AMRI D, DWIJAYANTI S. Comparison of control methods PD, PI, and PID on two wheeled self balancing robot [C]//Proceeding of International Conference on Electrical Engineering, Computer Science and Informatics. Yogyakarta, Indonesia: IEEE, 2014: 67–71.Google Scholar
- BATURE A A, BUYAMIN S, AHMAD M N, et al. A comparison of controllers for balancing two wheeled inverted pendulum robot [J]. International Journal of Mechanical & Mechatronics Engineering, 2014, 14(3): 62–68.Google Scholar
- ALARFAJ M, KANTOR G. Centrifugal force compensation of a two-wheeled balancing robot [C]//Proceeding of International Conference on Control, Automation, Robotics and Vision. Singapore: IEEE, 2010: 2333–2338.Google Scholar
- NASIR A N K, AHMAD M A, GHAZALI R, et al. Performance comparison between fuzzy logic controller (FLC) and PID controller for a highly nonlinear twowheels balancing robot [C]//2011 First International Conference on Informatics and Computational Intelligence. Bandung, Indonesia: IEEE, 2011: 176–181.CrossRefGoogle Scholar
- SKINNER B F. The behavior of organisms: An experimental analysis [M]. New York: D Appleton-Century Company, 1938.Google Scholar
- LEE D D, SEUNG H S. Learning in intelligent embedded systems [C]//Proceedings of the Embedded Systems Workshop. Cambridge, USA: IEEE, 1999: 133–139.Google Scholar