Development of an Effective Docking System for Modular Mobile Self-Reconfigurable Robots Using Extended Kalman Filter and Particle FilterReportar como inadecuado




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1

Mechanical and Mechatronics Engineering Department, University of Waterloo, Waterloo, ON, N2L 3G1, Canada

2

School of Engineering, University of Guelph, Guelph, ON, N1G 2W1, Canada





*

Author to whom correspondence should be addressed.



Academic Editor: Huosheng Hu

Abstract This paper presents an autonomous docking system with novel integrated algorithms for mobile self-reconfigurable robots equipped with inexpensive sensors. A novel docking algorithm was developed to determine the initial distance and orientation of the two modules, and sensor models were established through experiments. Both Extended Kalman filter EKF and particle filter PF were deployed to fuse the measurements from IR and encoders and provide accurate estimates of orientation and distance. Simulation experiments were carried out first and then real experiments were conducted to verify the feasibility and good performance of the proposed docking algorithm and system. The proposed system provides a robust and reliable docking solution using low cost sensors. View Full-Text

Keywords: modular mobile self-reconfigurable robots; autonomous docking; state estimation; extended Kalman filter; particle filtering; IR sensor modular mobile self-reconfigurable robots; autonomous docking; state estimation; extended Kalman filter; particle filtering; IR sensor





Autor: Peter Won 1, Mohammad Biglarbegian 2,* and William Melek 1

Fuente: http://mdpi.com/



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