Analysis of WiFi localization techniques for kidnapped robot problem
Nenhuma Miniatura disponível
Citações na Scopus
1
Tipo de produção
Artigo de evento
Data
2022-04-05
Autores
PEGORELLI NETO, A.
Flavio Tonidandel
Flavio Tonidandel
Orientador
Periódico
2022 IEEE International Conference on Autonomous Robot Systems and Competitions, ICARSC 2022
Título da Revista
ISSN da Revista
Título de Volume
Citação
PEGORELLI NETO, A.; TONIDANDEL, F. Analysis of WiFi localization techniques for kidnapped robot problem. 2022 IEEE International Conference on Autonomous Robot Systems and Competitions, ICARSC 2022, April, 2022.
Texto completo (DOI)
Palavras-chave
Resumo
© 2022 IEEE.This work proposes an analysis of the earliest indoor localization techniques based on recurrent neural networks (RNN) like Gated Recurrent Unit (GRU) and Long-Short Term Memory (LSTM), including k-Nearest Neighbors (KNN) machine learning, to process WiFi received signal strength data (RSS) for the kidnapped robot problem (KRP). The proposed solutions uses processed data generated in a Webots simulation of the iRobot Create robot, with the RSS signals simulated based on fingerprinting data from a real indoor area with 6 dedicated access points as reference. The efficiency of each system is evaluated using cumulative distribution function for several access point combinations, noise and vanishing levels for a model trained with the base test parameters from the reference material, with all 6 access points (APs) activated, ldBm Gaussian noise, 10% masking level and using 10 time steps of data as history inputs. The results show that RNN systems can achieve mean localization accuracy between $0.44\mathrm{m}\pm 0.39\mathrm{m}$ for LSTM and $0.50\mathrm{m}\pm 0.38\mathrm{m}$ for GRU and the KNN proposal reaching $0.68\mathrm{m}\pm 0.73\mathrm{m}$, proving the capability of those systems to recover from a KRP event keeping similar results obtained without any event.