Engenharia de Robôs
URI permanente desta comunidadehttps://repositorio.fei.edu.br/handle/FEI/339
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2 resultados
Resultados da Pesquisa
- Analysis of WiFi localization techniques for kidnapped robot problem(2022-04-05) PEGORELLI NETO, A.; Flavio Tonidandel© 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.
- DVG+A* and RRT Path-Planners: A Comparison in a Highly Dynamic Environment(2021) DA SILVA, COSTA, L.; Flavio Tonidandel© 2021, The Author(s), under exclusive licence to Springer Nature B.V. part of Springer Nature.This work provides a deeper comparison between two path planning algorithms, the Dynamic Visibility Graph A Star (DVG+A*) and Rapidly–exploring Random Trees (RRT), when applied in a high dimension and dynamic environment, which is the RoboCup Small Size League. The algorithms were compared under two different perspectives. In the first analysis, the algorithms were evaluated according to its computational time, path length and path safety in a static environment. Afterwards, they were evaluated regarding the accumulated computational time, number of recalculated paths, total navigation time and number of collisions in a dynamic environment. The static environment results have shown that the DVG+A* has a better overall performance than RRT, except for the path safety, however, some ideas on how to improve this were discussed. In the dynamic environment the algorithms performed similarly and with a high number of collisions during the experiments. Thus, showing the importance of using an obstacle avoidance algorithm combined with the path planner. In conclusion, the results obtained showed that both algorithms aren’t suitable for highly dynamic and cluttered environments, however, due how sparse the obstacles are in the SSL, they can still be used with some care. Regarding static environments, the DVG+A* has shown the best results.