Vol.:(0123456789) 1 3 Journal of Ambient Intelligence and Humanized Computing https://doi.org/10.1007/s12652-019-01331-0 ORIGINAL RESEARCH A lightweight ANN based robust localization technique for rapid deployment of autonomous systems Meetha V. Shenoy 1  · Anupama Karuppiah 2  · Narayan Manjarekar 2 Received: 13 January 2019 / Accepted: 18 May 2019 © Springer-Verlag GmbH Germany, part of Springer Nature 2019 Abstract The capability to localize or identify position in the feld 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 monitor- ing, 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 Diference 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 profling of the indoor deployment area under various NLOS conditions and mitigating NLOS afected 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 Artifcial 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 identifes 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 identifcation. 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 efciency. Keywords Localization · NLOS · LOS · Artifcial neural network · Ultrasound · UWB 1 Introduction The last decade witnessed an increasing trend in utilization of autonomous systems for several indoor applications such as greenhouse farming, warehouse transportation, Ambient Assisted Living Systems (AALS), search and rescue mis- sions, tracking of human beings, smart buildings etc. The ability to localize or identify the position of an object is an important requirement of any autonomous system, as data collected from any system has relevance only when associ- ated with a location stamp. SRS is an upcoming feld of research focusing on coordinated multi-robot systems, which is inspired by the self-organized behaviors of social organ- isms found in nature (Navarro et al. 2013). When compared to Multi-Robot Systems (MRS) systems, robots in SRS uti- lize local interactions among each other to produce complex and emergent behaviors which are beyond the capabilities of individual robots. SRS utilize decentralized, coopera- tive, cost-efective simple robots (Garnier et al. 2007). The SRS is scalable, robust, energy efcient and cost efective. SRS can potentially transform several applications such as AALS, search and rescue, warehouse transportation, sur- veillance, etc. Greater fexibility in the movement of robots, * Meetha V. Shenoy meetha.shenoy@pilani.bits-pilani.ac.in 1 Department of EEE, BITS Pilani Pilani Campus, Pilani, India 2 Department of EEE, BITS Pilani K K Birla Goa Campus, Sancoale, India