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A lightweight ANN based robust localization technique for rapid deployment of autonomous systems

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Abstract

The capability to localize or identify position in the field of deployment is a primary requirement of future autonomous system in domains such as warehouse transportation, ambient-assisted living/ health care systems, search and rescue, motion monitoring, etc. Although reliable indoor localization in the order of few centimeters can be achieved with the existing localization systems in Line-of-Sight (LOS) conditions, the localization under Non-line-of-Sight (NLOS) conditions is an open area of research. In range-based localization systems, distance estimation is a pre-requisite for location estimation. Time of Arrival (ToA) is considered to be the most accurate technique for distance estimation when compared to Time Difference of Arrival (TDoA) or Received Signal Strength Indication (RSSI). Most of the work available as literature on indoor localization under NLOS conditions is based on the profiling of the indoor deployment area under various NLOS conditions and mitigating NLOS affected timestamps from the ToA measurements. However, it is not practically possible to obtain a comprehensive data set containing all possible conditions of NLOS in indoor environments. In this paper, an Artificial Neural Network based Location Estimation Unit (ANN-LEU) based scheme is proposed to estimate the two-dimensional (2-D) location of an object under LOS and NLOS conditions. One of the unique features of the novel location estimation scheme is that the training of the system is required to be performed only under LOS conditions, thus facilitating the quick deployment in new environments. The proposed ANN-LEU is robust as it identifies the presence of NLOS if any, in the ToA measurements and thus removing false position estimations if any. The Mean Average Error (MAE) error in position estimated during the performance analysis of the proposed system was restricted to lesser than 20 cm, if the object is in range of three beacons in LOS, and also for the scenarios in which one of the three beacon nodes are in NLOS. The proposed scheme eliminates false position identification. The proposed scheme requires lesser number of beacons for localization when compared to the available indoor localization systems, thus also improving the cost and energy efficiency.

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Notes

  1. (x, y) positions are mentioned in this chapter in meters unless otherwise specified. The time of flight measurements in this chapter are mentioned in milliseconds unless otherwise specified.

References

  • Alarifi A, Al-Salman A, Alsaleh M, Alnafessah A, Al-Hadhrami S, Al-Ammar M, Al-Khalifa H (2016) Ultra wideband indoor positioning technologies: analysis and recent advances. Sensors 16(5):707–712

    Article  Google Scholar 

  • Albuquerque D, Vieira J, Bastos C (2009) Room acoustics simulator for ultrasonic robot location. Robótica 4(77):10–14

    Google Scholar 

  • Casas R, Marco A, Guerrero JJ, Falcó J (2006) Robust estimator for non-line-of-sight error mitigation in indoor localization. Eurasip J Appl Signal Process 2006:1–8

    Google Scholar 

  • Csáji B (2001) Approximation with artificial neural networks, MSc. thesis, Faculty of Science, Eötvös Loránd University (ELTE-TTK), Budapest, Hungary, pp  45–47

  • Demuth H, Beale M (2006) Neural network toolbox. Mathworks Inc 19(1):1–7

  • Fu G, Zhang J, Chen W, Peng F, Yang P, Che C (2013) Precise localization of mobile robots via odometry and wireless sensor network. Int J Adv Robot Syst 10:1–13

    Article  Google Scholar 

  • Garnier S, Gautrais J, Theraulaz G (2007) The biological principles of swarm intelligence. Swarm Intell 1(1):3–31

    Article  Google Scholar 

  • Ghaffari A, Abdollahi H, Khoshayand MR, Bozchalooi IS, Dadgar A, Rafiee-tehrani M (2006) Performance comparison of neural network training algorithms in modeling of bimodal drug delivery. Int J Pharm 327:126–138

    Article  Google Scholar 

  • Texas Instruments (2019) CC2500 Low-cost low-power 2.4 Ghz RF Transceiver. Texas Instruments, DataSheet

  • Janglová D (2004) Neural networks in mobile robot motion. Int J Adv Robot Syst 1(1):15–22

    Article  Google Scholar 

  • Kolanowski K, Swietlicka A, Kapela R, Pochmara J (2017) Multisensor data fusion using Elman neural networks. Appl Math Comput 319:236–244

    Article  MathSciNet  Google Scholar 

  • Lazik P, Rajagopal N, Shih O, Sinopoli B, Rowe A (2015) ALPS: a bluetooth and ultrasoundplatform for mapping and localization. In: Proceedings of the 13th ACM conference on embedded networked sensor systems (SenSys), pp 73–84

  • Mainetti L, Patrono L, Sergi I (2014) A survey on indoor positioning systems. In: Proceedings of the 22nd international conference on software, telecommunications and computer networks (SoftCOM), pp 111–120

  • Moravek P, Komosny D, Simek M, Girbau D (2011) Measurement with the cricket localization system. Elektrorevue 2(2):1–6

    Google Scholar 

  • Navarro I, Matía Fernando (2013) An introduction to swarm robotics. ISRN Robot 2013:1–10

    Article  Google Scholar 

  • Salamah AH, Tamazin M, Sharkas MA, Khedr M (2017) An enhanced WiFi indoor localization system based on machine learning. In: Proceedings of the international conference on indoor positioning and indoor navigation (IPIN), pp 1–8

  • Sathyan T, Hedley M, Mallick M (2010) An analysis of the error characteristics of two time of arrival localization techniques. In: 13th international conference on information fusion, pp 1–7

  • Shenoy MV, Anupama KR (2018) Swarm-sync: a distributed global time synchronization framework for swarm robotic systems. Pervasive Mobile Comput 44:1–30

    Article  Google Scholar 

  • Shenoy MV, Anupama KR, Manjrekar N (2017) Indoor localization in NLOS conditions using asynchronous WSN and neural network. In: Proceedings of the international conference on communication and signal processing (ICCSP), pp 2130–2134

  • STMicroelectronics (2016) Document ID 022152 Rev 3-STM32F405xx, STM32F407xx Datasheet, (May)

  • Svozil D, Kvasnička V, Pospíchal J (1997) Introduction to multi-layer feed-forward neural networks. Chemom Intell Lab Syst 1:43–62

    Article  Google Scholar 

  • Tang J, Varley MR, Peak MS (1997) Hardware implementations of multi-layer feedforward neural networks and error backpropagation using 8-bit PIC microcontrollers. In: IEEE colloquium on neural and fuzzy systems: design, hardware and applications (Digest No: 1997/133), pp 2–5

  • Zafari F, Gkelias A, Leung KK (2017) A survey of indoor localization systems and technologies. CoRR arXiv:abs/1709.01015:~1–32

  • Zhang L, Li Y, Gu Y, Yang W (2017) An efficient machine learning approach for indoor localization. China Commun 14(11):141–150

    Article  Google Scholar 

  • Zhu J, Sutton P (2003) FPGA implementations of neural networks—a survey of a decade of progress. Field programmable logic and application. Springer, Berlin, pp 1062–1066

    Google Scholar 

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Authors and Affiliations

  1. Department of EEE, BITS Pilani Pilani Campus, Pilani, India

    Meetha V. Shenoy

  2. Department of EEE, BITS Pilani K K Birla Goa Campus, Sancoale, India

    Anupama Karuppiah & Narayan Manjarekar

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  1. Meetha V. Shenoy
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  2. Anupama Karuppiah
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  3. Narayan Manjarekar
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Correspondence to Meetha V. Shenoy.

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Shenoy, M.V., Karuppiah, A. & Manjarekar, N. A lightweight ANN based robust localization technique for rapid deployment of autonomous systems. J Ambient Intell Human Comput 11, 2715–2730 (2020). https://doi.org/10.1007/s12652-019-01331-0

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  • DOI: https://doi.org/10.1007/s12652-019-01331-0

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