article

Mobile robot localization based on PSO estimator

Author:

Ramazan HavangiAuthors Info & Claims

Asian Journal of Control, Volume 21, Issue 4

Pages 2167 - 2178

https://doi.org/10.1002/asjc.2004

Published: 08 October 2019 Publication History

Abstract

Localization is fundamental to autonomous operation of the mobile robot. A particle filter (PF) is widely used in mobile robot localization. However, the robot localization based PF has several limitations, such as sample impoverishment and a degeneracy problem, which reduce significantly its performance. Evolutionary algorithms, and more specifically their optimization capabilities, can be used in order to overcome PF based on localization weaknesses. In this paper, mobile robot localization based on a particle swarm optimization (PSO) estimator is proposed. In the proposed method, the robot localization converts dynamic optimization to find the best robot pose estimate, recursively. Unlike the localization based on PF, the resampling step is not required in the proposed method. Moreover, it does not require noise distribution. It searches stochastically along the state space for the best robot pose estimate. The results show that the proposed method is effective in terms of accuracy, consistency, and computational cost compared with localization based on PF and EKF.

References

[1]

H. Fourati et al., Complementary observer for body segments motion capturing by inertial and magnetic sensors, IEEE/ASME Trans. Mechatron., 19 (1) (2014), 149–157.

[2]

J. Wu et al., Fast linear quaternion attitude estimator using vector observations, IEEE Trans. Autom. Sci. Eng., 15 (1) (2018), 307–319.

[3]

J. Pomárico‐Franquiz, S. H. Khan, Y. S. Shmaliy, Combined extended FIR/Kalman filtering for indoor robot localizationvia triangulation, Meas, 50 (2014), 236–243.

[4]

K. Bader, B. Lussier, W. Schönm, A fault tolerant architecture for data fusion: A real application of Kalman filters for mobile robot localization, Robot Auton. Syst., 88 (2017), 11–23.

Digital Library

[5]

J. Liu, B. Cai, Y. Wang, Particle swarm optimization for vehicle positioning based on robust cubature Kalman filter, Asian J. Control, 17 (2) (2014), 648–663.

[6]

K. Xiong and C. Wei, Adaptive iterated extended KALMAN filter for relative spacecraft attitude and position estimation, Asian J. Control, 20 (4) (2017), 1595–1610.

[7]

Z. Zhou et al., Critical issues on Kalman filter with colored and correlated system noises, Asian J. Control, 19 (6) (2017), 1905–1919.

[8]

A. Yu et al., An improved dual unscented Kalman filter for state and parameter estimation, Asian J. Control, 18 (4) (2016), 1427–1440.

Digital Library

[9]

C. J. Sun, H. Y. Kuo, C. E. Lin, A sensor based indoor mobile localization and navigation using unscented Kalman filter, IEEE on Position Location and Navigation Symp. (PLANS), Indian Wells, CA, USA, 2010, 327–331.

[10]

S. Yousefi, X. W. Chang, B. Champagne, Mobile localization in non‐line‐of‐sight using constrained square‐root unscented Kalman filter, IEEE Trans. Veh. Technol., 64 (5) (2015), 2071–2083.

[11]

J. Zheng‐Wei and G. Yuan‐Tao, Novel adaptive particle filters in robot localization, J. Acta Automatica. Sinica, 31 (6) (2005), 833–838.

[12]

H. Nurminen, A. Ristimaki, S. Ali‐Loytty, R. Piché, Particle filter and smoother for indoor localization, in Proc. Int. Conf. on Indoor Positioning and Indoor Navigation (IPIN), IEEE, Montbeliard‐Belfort, France, (2013) 1–10.

[13]

D. Zhuo‐hua et al., An adaptive particle filter for mobile robot fault diagnosis, J. Cent. South Univ., 13 (6) (2006), 689–693.

[14]

P. Wu, Q. Luo, L. Kong, Cooperative localization of network robot system based on improved MPF, Proc. of the IEEE Int. Conf. on Inform. and Automation, Ningbo, China, August 2016.

[15]

B.‐F. Wu and C.‐L. Jen, Particle‐filter‐based radio localization for mobile robots in the environments with low‐density WLAN aps, IEEE Trans. Ind. Electron., 61 (12) (2015), 6860–6870.

[16]

S. Thrun et al., Robust Monte Carlo localization for mobile robots, J. Artif. Intell., 128 (1–2) (2001), 99–141.

Digital Library

[17]

A. A. Wardhana, E. Clearesta, A. Widyotriatmo, Mobile robot localization using modified particle filter, 3rd Int. Conf. on Instrumentation Control and Automation (ICA), Bali, Indonesia, August 28–30, 2013.

[18]

G. Cen, N. Matsuhira, J. Hirokawa, H. Ogawa, I. Hagiwara, Mobile robot global localization using particle filters, Int. Conf. on Control, Automation and Syst., Seoul, South Korea, 2008.

[19]

Z. Chen et al., A multiple model tracking algorithm based on an adaptive particle filter, Asian J. Control, 18 (5) (2016), 1877–1189.

Digital Library

[20]

P. Wu et al., An improved marginal particle filter with proposal distribution, J. Chinese Comput. Syst., 37 (2) (2016), 381–384.

[21]

D. Simon, Optimal state estimation Kalman, H∞ and nonlinear approaches, John Wiley and Sons, Inc, 2006.

[22]

M. Sanjeev Arulampalam et al., A tutorial on particle filters for online nonlinear/Non‐Gaussian Bayesian tracking, IEEE Trans. Signal Process. (S1053‐587X), 50 (2) (2002), 174–188.

Digital Library

[23]

L. Xiaolong, F. Jinfu, L. Qian, L. Taorong, L. Bingjie, A swarm intelligence optimization for particle filter, Proc. 7th World Congress on Intell. Control and Automation, Chongqing, China, June 25–27, 2008.

[24]

G. Tong, Z. Fang, X. Xu, A particle swarm optimized particle filter for nonlinear system state estimation, IEEE Congr. on Evolutionary Computation. Sheraton Vancouver Wall Centre Hotel, Vancouver, BC, Canada, July 16–21, 2006.

[25]

G. Zhang, Y. Cheng, F. Yang, Q. Pan, Particle filter based on PSO, Int. Conf. on Intell. Computation Technol. and Automation, Hunan, China, 2008.

[26]

S. Chattaraj, A. Mukherjee, Efficient estimation of system states of a poorly modeled 2‐D target tracking system using evolutionary strategy based particle filter algorithm, IEEE First Int. Conf. on Control, Meas. and Instrumentation (CMI), Kolkata, India, 2016.

[27]

R. Zhou, M. Wu, K. Zhou and J. Teng, A particle filter resampling method based on improved genetic algorithm, IEEE Chinese Guidance, Navigation and Control Conf. (CGNCC), Jinan, China, 2017.

[28]

C. Huemmer et al., Estimating parameters of nonlinear systems using the elitistparticle filter based on evolutionary strategies, IEEE/ACM Trans. on Audio, Speech, Lang. Proc., 26 (3) (2018), 595–608.

Digital Library

[29]

R. Havangi, M. A. Nekoui, M. Teshnehlab, A multi swarm particle filter for mobile robot localization, Int. J. Comput. Sci. Issues, 7 (2) (2010), 15–22.

[30]

A. Khorshidi, A. Mohammad Shahri, Evolutionary particle filter applied to leader‐labor multi‐robot localization for communication failure and kidnapped situations, Int. Conf. on Robot. and Mechatronics, Tehran, Iran, October 26–28, 2016.

[31]

T. J. M. Sanguino and F. P. Gómez, Toward simple strategy for optimal tracking and localization of robots with adaptive particle filtering, IEEE/ASME Trans. Mechatron., 21 (6) (2016), 2793–2804.

[32]

L. Moreno et al., A genetic algorithm for mobile robot localization using ultrasonic sensors, J Intell Robot Syst, 34 (2002), 135–154.

Digital Library

[33]

N. M. Kwok, D. K. Liu, G. Dissanayake, Evolutionary computing based mobile robot localization, Eng. Appl. Artif. Intel., 19 (2006), 857–868.

Digital Library

[34]

K. Wu et al., Swarm intelligent based particle filter for alternating talker localization and tracking using microphone arrays, IEEE/ACM Trans. Audio, Speech, Language Proc., 25 (6) (2017), 1384–1397.

Digital Library

[35]

S. Wang et al., An optimization based moving horizon estimation with application to localization of autonomous underwater vehicles, Robot. Auton. Syst., 62 (10) (2014), 1581–1596.

Digital Library

[36]

Y. Xia and Y. Yang, In Mobile robot localization method based on adaptive particle filter, C. Xiong et al. (eds.), Springer‐Verlag, Berlin Heidelberg, 2008, 963–972.ICIRA 2008, Part I, LNAI 5314

[37]

J. Liu, B. Cai, Y. Wang, Particle swarm optimization for vehicle positioning based on robust cubature Kalman filter, Asian J. Control, 17 (2) (2015), 648–663.

[38]

Y. Maeda et al., On simultaneous perturbation particle swarm optimization, CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation, Trondheim, Norway, (2009), 3271–3276.

[39]

J. C. Spall, Implementation of the simultaneous perturbation algorithm for stochastic optimization, IEEE Trans. Aerosp. Electron. Syst., 34 (3) (1998), 817–823.

[40]

E. Nebot, Victoria Park Data Set. [Online]. Available: http://www‐personal.acfr.usyd.edu.au/nebot/dataset.htm

Cited By

Eder MReip MSteinbauer G(2022)Creating a robot localization monitor using particle filter and machine learning approachesApplied Intelligence10.1007/s10489-020-02157-652:6(6955-6969)Online publication date: 1-Apr-2022
https://dl.acm.org/doi/10.1007/s10489-020-02157-6
Lu YKarimi H(2021)Variance‐constrained resilient H filtering for mobile robot localization under dynamic event‐triggered communication mechanismAsian Journal of Control10.1002/asjc.258123:5(2064-2078)Online publication date: 14-Jul-2021
https://dl.acm.org/doi/10.1002/asjc.2581
Juan CHu J(2021)Single‐object localization using multiple ultrasonic sensors and constrained weighted least‐squares methodAsian Journal of Control10.1002/asjc.249123:3(1171-1184)Online publication date: 21-Mar-2021
https://dl.acm.org/doi/10.1002/asjc.2491
Show More Cited By

Index Terms

Mobile robot localization based on PSO estimator

Index terms have been assigned to the content through auto-classification.

Recommendations

An improved particle filter for mobile robot localization based on particle swarm optimization
Highlights
- Accurate and robust global localization is achieved using only laser sensors.
- ...
Abstract
As one of the most important issues in the field of mobile robotics, self-localization allows a mobile robot to identify and keep track of its own position and orientation as the robot moves through the environment. In this work, a ...
PSO-FastSLAM: an improved FastSLAM framework using particle swarm optimization
SMC'09: Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics

FastSLAM is a framework which solves the problem of simultaneous localization and mapping using a Rao-Blackwellized particle filter. Conventional FastSLAM is known to degenerate over time in terms of accuracy due to the particle depletion in resampling ...
Fuzzy adaptive particle filter for localization of a mobile robot
KES'07/WIRN'07: Proceedings of the 11th international conference, KES 2007 and XVII Italian workshop on neural networks conference on Knowledge-based intelligent information and engineering systems: Part III

Localization is one of the important topics in robotics and it is essential to execute a mission. Most problems in the class of localization are due to uncertainties in the modeling and sensors. Therefore, various filters are developed to estimate the ...

Comments

Information & Contributors

Information

Published In

cover image Asian Journal of Control

Asian Journal of Control Volume 21, Issue 4

July 2019

564 pages

ISSN:1561-8625

EISSN:1934-6093

DOI:10.1002/asjc.v21.4

Issue’s Table of Contents

© 2019 Chinese Automatic Control Society and John Wiley & Sons Australia, Ltd.

Publisher

John Wiley & Sons, Inc.

United States

Publication History

Published: 08 October 2019

Author Tags

Qualifiers

Article

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

4
Total Citations
View Citations
0
Total Downloads

Downloads (Last 12 months)0
Downloads (Last 6 weeks)0

Reflects downloads up to 29 Jul 2024

Other Metrics

View Author Metrics

Citations

Cited By

Eder MReip MSteinbauer G(2022)Creating a robot localization monitor using particle filter and machine learning approachesApplied Intelligence10.1007/s10489-020-02157-652:6(6955-6969)Online publication date: 1-Apr-2022
https://dl.acm.org/doi/10.1007/s10489-020-02157-6
Lu YKarimi H(2021)Variance‐constrained resilient H filtering for mobile robot localization under dynamic event‐triggered communication mechanismAsian Journal of Control10.1002/asjc.258123:5(2064-2078)Online publication date: 14-Jul-2021
https://dl.acm.org/doi/10.1002/asjc.2581
Juan CHu J(2021)Single‐object localization using multiple ultrasonic sensors and constrained weighted least‐squares methodAsian Journal of Control10.1002/asjc.249123:3(1171-1184)Online publication date: 21-Mar-2021
https://dl.acm.org/doi/10.1002/asjc.2491
Shafei HBahrami M(2021)Trajectory tracking control of a wheeled mobile robot in the presence of matched uncertainties via a composite control approachAsian Journal of Control10.1002/asjc.241823:6(2805-2823)Online publication date: 1-Nov-2021
https://dl.acm.org/doi/10.1002/asjc.2418

View Options

View options

Get Access

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

Media

Figures

Other

Tables

View Issue’s Table of Contents