Development of an Effective Docking System for Modular Mobile Self-Reconfigurable Robots Using Extended Kalman Filter and Particle Filter
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...
Main Authors: | , , |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2015-02-01
|
Series: | Robotics |
Subjects: | |
Online Access: | http://www.mdpi.com/2218-6581/4/1/25 |
id |
doaj-f5cc71f34e62428b8b6768283976dd42 |
---|---|
record_format |
Article |
spelling |
doaj-f5cc71f34e62428b8b6768283976dd422020-11-24T23:26:13ZengMDPI AGRobotics2218-65812015-02-0141254910.3390/robotics4010025robotics4010025Development of an Effective Docking System for Modular Mobile Self-Reconfigurable Robots Using Extended Kalman Filter and Particle FilterPeter Won0Mohammad Biglarbegian1William Melek2Mechanical and Mechatronics Engineering Department, University of Waterloo, Waterloo, ON, N2L 3G1, CanadaSchool of Engineering, University of Guelph, Guelph, ON, N1G 2W1, CanadaMechanical and Mechatronics Engineering Department, University of Waterloo, Waterloo, ON, N2L 3G1, CanadaThis 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.http://www.mdpi.com/2218-6581/4/1/25modular mobile self-reconfigurable robotsautonomous dockingstate estimationextended Kalman filterparticle filteringIR sensor |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Peter Won Mohammad Biglarbegian William Melek |
spellingShingle |
Peter Won Mohammad Biglarbegian William Melek Development of an Effective Docking System for Modular Mobile Self-Reconfigurable Robots Using Extended Kalman Filter and Particle Filter Robotics modular mobile self-reconfigurable robots autonomous docking state estimation extended Kalman filter particle filtering IR sensor |
author_facet |
Peter Won Mohammad Biglarbegian William Melek |
author_sort |
Peter Won |
title |
Development of an Effective Docking System for Modular Mobile Self-Reconfigurable Robots Using Extended Kalman Filter and Particle Filter |
title_short |
Development of an Effective Docking System for Modular Mobile Self-Reconfigurable Robots Using Extended Kalman Filter and Particle Filter |
title_full |
Development of an Effective Docking System for Modular Mobile Self-Reconfigurable Robots Using Extended Kalman Filter and Particle Filter |
title_fullStr |
Development of an Effective Docking System for Modular Mobile Self-Reconfigurable Robots Using Extended Kalman Filter and Particle Filter |
title_full_unstemmed |
Development of an Effective Docking System for Modular Mobile Self-Reconfigurable Robots Using Extended Kalman Filter and Particle Filter |
title_sort |
development of an effective docking system for modular mobile self-reconfigurable robots using extended kalman filter and particle filter |
publisher |
MDPI AG |
series |
Robotics |
issn |
2218-6581 |
publishDate |
2015-02-01 |
description |
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. |
topic |
modular mobile self-reconfigurable robots autonomous docking state estimation extended Kalman filter particle filtering IR sensor |
url |
http://www.mdpi.com/2218-6581/4/1/25 |
work_keys_str_mv |
AT peterwon developmentofaneffectivedockingsystemformodularmobileselfreconfigurablerobotsusingextendedkalmanfilterandparticlefilter AT mohammadbiglarbegian developmentofaneffectivedockingsystemformodularmobileselfreconfigurablerobotsusingextendedkalmanfilterandparticlefilter AT williammelek developmentofaneffectivedockingsystemformodularmobileselfreconfigurablerobotsusingextendedkalmanfilterandparticlefilter |
_version_ |
1725555935675416576 |