MOBİL ROBOTLARIN BİNA İÇİ KOŞULLARDA ULAŞMA ZAMANI KULLANILARAK KABLOSUZ LOKALİZASYONU

Year 2018, , 99 - 119, 31.01.2018

Muzaffer Kanaan , Zeynel Abidin Kuş

https://doi.org/10.28948/ngumuh.364850

Abstract

   Bu çalışma bina içi koşullarda çalışmak
durumunda olan mobil robotların ulaşma zamanı tekniği kullanılarak kablosuz
lokalizasyonu hakkındadır. Binanın içinde belli noktalarda konumlandırılmış
referans noktaları vasıtası ile robotun bu noktalara olan mesafesinin ulaşma
zamanı metodu ile tespiti ve buna bağlı olarak robotun konumunun tespiti
(lokalizasyonu) hedeflenmektedir. Çalışmanın literatüre somut katkıları şu
şekilde özetlenebilir. İlk olarak mobil robotun referans noktalarına göre
hareketi sonucu değişen kanal koşullarına bağlı olarak değişken mesafe ölçüm
hatalarının olduğu durumlarda iki farklı lokalizasyon algoritmasının (en küçük
kareler ve ağırlıklı en küçük kareler) performansları karşılaştırmalı olarak
analiz edilmiştir. İkinci olarak mesafe ölçümlerindeki hataları mesafe
ölçümleri lokalizasyon algoritmaları tarafından kullanılmadan önce azaltmayı
hedefleyen bir hata kestirim metodu önerilmiş, bahsedilen lokalizasyon
algoritmalarının performansı bu metot ile beraber analiz edilmiştir. Elde
edilen sonuçlar hata kestirim metodu ile beraber kullanıldığında her iki
algoritmanın performansının sistem senaryolarına bağlı olarak %5-17
oranında iyileştirilebileceğini göstermektedir.

Keywords

Mobil robot, ulaşma zamanı

WIRELESS INDOOR LOCALIZATION OF MOBILE ROBOTS USING TIME OF ARRIVAL

Abstract

   This work focuses on wireless localization
of a mobile robot operating inside a building using the time of arrival
technique. The basic idea is to perform measurements of range (or distance)
between the robot and reference points located at specific points inside the
building by means of time of arrival measurements and using these measurements
to estimate the location of the robot. The contributions of this work can be summarized
as follows. First, the performance of two localization algorithms (namely
least-squares and weighted least-squares) are compared in the presence of
ranging errors that fluctuate as a result of mobile robot movement with respect
to the reference points. Second, an error estimation method is proposed, the
goal of which is to reduce the error in the range measurements prior to their
use with localization algorithms. The performance of the aforementioned
localization algorithms, working in conjunction with this error estimation
method, are analysed. The results clearly indicate that the performance of both
algorithms can be improved by approximately 5-17% depending on system
scenarios.

Keywords

Mobile robot, time of arrival

References

• [1] ALBERT, F.Y.C., MASON, C.H.S., KIING, C.K.J., EE K.S., CHAN, K.W., “Remotely Operated Solar-powered Mobile Metal Detector Robot”, Procedia Computer Science, 42, 232-239, 2014.
• [2] PALACÍN, J., SALSE, J.A., VALGAÑÓN, I., CLUA, X., “Building a Mobile Robot for a Floor-Cleaning Operation in Domestic Environments”, IEEE Transactions on Instrumentation and Measurement, 53, 1418-1424, 2004.
• [3] YAKOUBI, M.A., LASKRI, M.T., “The Path Planning of Cleaner Robot for Coverage Region Using Genetic Algorithms”, Journal of Innovation in Digital Ecosystems, 3, 37-43, 2016.
• [4] FORLIZZI, J., DISALVO C., “Service Robots in the Domestic Environment: A Study of the Roomba Vacuum in the Home”, Proceedings of the 1st ACM SIGCHI/SIGART conference on Human-robot interaction. 258-265. Salt Lake City, USA, 2006.
• [5] EMKEN, J.L., WYNNE, J.H., HARKEMA, S.J., REINKENSMEYER, D.J., “A Robotic Device for Manipulating Human Stepping”, IEEE Transactions on Robotics, 22,185-189, 2006.
• [6] KANEKO, S., CAPI, G., “Human-Robot Communication for Surveillance of Elderly People in Remote Distance”, IERI Procedia, 10, 92-97, 2014.
• [7] CHIVAROV, N., SHIVAROV, N., “Remote Control User Interfaces for Service Mobile Robots for Elderly Care”, IFAC-PapersOnLine, 49, 73-76, 2016.
• [8] CHIVAROV, N., CHIKURTEV, D., YOVCHEV, K., SHIVAROV, S., “Cost-Oriented Mobile Robot Assistant for Disabled Care”, IFAC-PapersOnLine, 48, 128-133, 2015.
• [9] PIGNOLO, L., “Robotics in Neuro-Rehabilitation”, Journal of Rehabilitation Medicine, 41, 955-960, 2009.
• [10] SUMIYA, T., MATSUBARA, Y., NAKANO, M., SUGAYA, M., “A Mobile Robot for Fall Detection for Elderly-Care”, Procedia Computer Science, 60, 870-880, 2015.
• [11] MOHAMED, Z., CAPI, G., “Development of a New Mobile Humanoid Robot for Assisting Elderly People”, Procedia Engineering, 41, 345-351, 2012.
• [12] ROSSETTI, M.D., KUMAR, A., FELDER, R.A., “Mobile Robot Simulation of Clinical Laboratory Deliveries”, Proceedings of the 30th Conference on Winter Simulation, 1415-1422. Washington, DC, USA, 2000.
• [13] ZHANG, X., GAO, H., GUO, M., LI, G., LIU, Y., LI, D., “A Study on Key Technologies of Unmanned Driving”, CAAI Transactions on Intelligence Technology, 1, 4-13, 2016.
• [14] GRUDIC, G.Z., LAWRENCE, P.D., “A Nonparametric Learning Approach to Vision based Mobile Robot Localization”, Proceedings of the IEEE/RSJ International Conference, 724-729. Victoria, BC, Canada, 1998.
• [15] LEE, S., SONG, J.-B., “Mobile Robot Localization using Optical Flow Sensors”, International Journal of Control, Automation and Systems, 2, 485-493, 2004.
• [16] HATİPOĞLU, K.H., Genişletilmiş Kalman Süzgeci ile Gezgin Robot Konumunun Belirlenmesi, Yüksek Lisans Tezi, Başkent Üniversitesi, Fen Bilimleri Enstitüsü, Ankara, Türkiye, 2007.
• [17] ROUMELIOTIS, S., BEKEY, G.A., “Bayesian Estimation and Kalman Filtering: A Unified Framework for Mobile Robot Localization”, Proceedings of the 2000 IEEE International Conference on Robotics and Automation, 2985-2992. San Francisco, USA, 2000.
• [18] LIN, H-H., TSAI, C-C., HSU, J-C., “Ultrasonic Localization and Pose Tracking of an Autonomous Mobile Robot via Fuzzy Adaptive Extended Information Filtering”, IEEE Transactions on Instrumentation and Measurement, 57, 2024-2034, 2008.
• [19] METREAUD, L.T., An RF-Isolated Real-Time Multipath Testbed for Performance Analysis of WLANs, Yüksek Lisans Tezi, Worcester Polytechnic Institute, Worcester, USA, 2006.
• [20] HEIDARI, M., Identification and Modeling of the Dynamic Behavior of The Direct Path Component in ToA-Based Indoor Localization Systems, Doktora Tezi, Worcester Polytechnic Institute, Worcester, USA, 2008.
• [21] ALAVI, B., PAHLAVAN, K., “Modeling of the TOA-based Distance Measurement Error Using UWB Indoor Radio Measurements”, IEEE Communications Letters, 10, 275-277, 2006.
• [22] NERGUIZIAN, C., DESPINS, C., AFFÈS, S., “A Framework for Indoor Geolocation Using and Intelligent System”, Proceedings of the 3rd IEEE Workshop on WLANs, 1-5. Boston, USA, 2001.
• [23] ALAVI, B., Distance Measurement Error Modeling for Time-of-Arrival Based Indoor Geolocation, Doktora Tezi, Worcester Polytechnic Institute, Worcester, USA, 2006.
• [24] KANAAN, M., PAHLAVAN, K., “Algorithm For TOA-Based Indoor Geolocation”, IEE Electronics Letters, 40, 1421-1422, 2004.
• [25] BAHL, P., PADMANABDAN, V.N., “RADAR: An In-building RF-based User Location and Tracking System”, Proceedings of the Nineteenth Annual Joint Conference of the IEEE Computer and Communications Societies, 775-784. Tel Aviv, Israel, 2000.
• [26] KANAAN, M., Node Density and Quality of Estimation for Infrastructure-based Indoor Geolocation Using Time of Arrival, Doktora Tezi, Worcester Polytechnic Institute, Worcester, USA, 2008.
• [27] SAYED, A.H., TARIGHAT, A., KHAJEHNOURI, N., “Network-based Wireless Location: Challenges Faced in Developing Techniques for Accurate Wireless Location Information”, IEEE Signal Processing Magazine, 22, 24-40, 2005.
• [28] ALSINDI, N., Indoor Cooperative Localization for Ultra Wideband Wireless Sensor Networks, Doktora Tezi, Worcester Polytechnic Institute, Worcester, USA, 2008.
• [29] KANAAN, M., PAHLAVAN, K., “A Comparison of Wireless Geolocation Algorithms in the Indoor Environment”, Proceedings of the IEEE Wireless Communications and Networking Conference, 177-182. Atlanta, USA, 2004.
• [30] DAVIDON, W.C., “Variance Algorithm for Minimization”, Computer Journal, 10, 406-410, 1968.
• [31] CHEN, P.C., Mobile Position Location Estimation in Cellular Systems, Doktora Tezi, Rutgers University, New Jersey, USA, 1999.
• [32] CHEN, P.C., “A Non-Line-of-Sight Error Mitigation Algorithm in Location Estimation”, Proceedings of the IEEE Wireless Communications and Networking Conference, 316-320. New Orleans, USA, 1999.
• [33] VAN TREES, H.L., Detection, Estimation and Modulation Theory Part I, John Wiley & Sons, Inc., New York, USA, 2001.
• [34] KUŞ, Z.A., Mobil Robotların Bina İçi Koşullarda Ulaşma Zamanı Kullanılarak Kablosuz Konumlandırılması, Yüksek Lisans Tezi, Erciyes Üniversitesi, Fen Bilimleri Enstitüsü, Kayseri, Türkiye, 2013.
• [35] AMIOT, N., PEDERSEN, T., LAARAIEDH, M., UGUEN, B., “A Hybrid Positioning Method Based on Hypothesis Testing”, IEEE Wireless Communications Letters, 1, 348-351, 2012.
• [36] SOLTANMOHAMMADI, E., OROOJI, M., NARAGHI-POUR, M., “Decentralized Hypothesis Testing in Wireless Sensor Networks in the Presence of Misbehaving Nodes”, IEEE Transactions on Information Forensics and Security, 8, 205-215, 2013.
• [37] IYENGAR, S.G., NIU, R., VARSHNEY, P.K., “Fusing Dependent Decisions for Hypothesis Testing with Heterogeneous Sensors”, IEEE Transactions on Signal Processing, 60, 4888-4897, 2012.
• [38] https://www.mathworks.com/help/stats/coefficient-of-determination-r-squared.html (erişim tarihi: 24.08.2017).