Abstract
A tri-wheeled omnidirectional microrobot actuated by 3mm electromagnetic micromotors is described. Since it is designed for microassembly in a microfactory, the positioning precision becomes a key parameter. However, with traditional PID control, the microrobot positioning precision is not high during a bearing/axis assembly task. This paper presents a reinforcement learning (RL) algorithm based on the on-policy (Sarsa(λ)) method using linear function approximation. The algorithm is used to generate an optimal path by controlling the choice of four moving actions of the microrobot. The aim is to reach the target position with high positioning precision. Simulations show that this RL algorithm is able to greatly improve the positioning precision with regard to the global path optimization.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Eisinberg, A., Menciassi, A., Dario, P., et al.: Teleoperated assembly of a micro-lens system by means of a micro-manipulation workstation. Assembly Automation 27(2), 123–133 (2007)
Driesen, W., Varidel, T., Mazerolle, S., et al.: Flexible micromanipulation platform based on tethered cm3-sized mobile micro robots. In: IEEE International Conference on Robotics and Biomimetics, pp. 145–150 (2005)
Martel, S.: Fundamental principles and issues of high-speed piezoactuated three-legged motion for miniature robots designed for nanometer-scale operations. The International Journal of Robotics Research 24(7), 575–588 (2005)
Kawano, H.: Method for applying reinforcement learning to motion planning and control of under-actuated underwater vehicle in unknown non-uniform sea flow. In: IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 996–1002 (2005)
Zhuang, X.D., Meng, Q.C., Wei, T.B., et al.: Robot path planning in dynamic environment based on reinforcement learning. Journal of Harbin Institute of Technology (New Series) 8(3), 253–255 (2001)
Duan, Y., Xu, X.H.: Fuzzy reinforcement learning and its application in robot navigation. In: Proceedings of the Fourth International Conference on Machine Learning and Cybernetics, Guangzhou, p. 899–904 (2005)
Li, J.H., Li, Z.B., Chen, J.P.: An Omni-directional Mobile Millimeter-sized Micro-robot with 3-mm Electromagnetic Micro-motors for a Micro-factory. Advanced Robotics 21(12), 1369–1391 (2007)
Sutton, R.S., Barto, A.G.: Reinforcement Learning: An Introduction, pp. 193–212. MIT Press, Cambridge (1998)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Li, J., Li, Z., Chen, J. (2008). Reinforcement Learning Based Precise Positioning Method for a Millimeters-Sized Omnidirectional Mobile Microrobot. In: Xiong, C., Huang, Y., Xiong, Y., Liu, H. (eds) Intelligent Robotics and Applications. ICIRA 2008. Lecture Notes in Computer Science(), vol 5314. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-88513-9_101
Download citation
DOI: https://doi.org/10.1007/978-3-540-88513-9_101
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-88512-2
Online ISBN: 978-3-540-88513-9
eBook Packages: Computer ScienceComputer Science (R0)