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Robotics 2015, 4(1), 25-49; doi:10.3390/robotics4010025

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

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
Received: 25 November 2014 / Revised: 3 February 2015 / Accepted: 3 February 2015 / Published: 12 February 2015
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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
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Won, P.; Biglarbegian, M.; Melek, W. Development of an Effective Docking System for Modular Mobile Self-Reconfigurable Robots Using Extended Kalman Filter and Particle Filter. Robotics 2015, 4, 25-49.

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