ISBN
Formato digital
979-13-87837-54-9
Fecha de publicación
06-10-2025
Licencia
D. R. © Copyright 2025. Alma Y. Alanis, Jorge Galvez, Omar Avalos, Eduardo Méndez-Palos, Jorge D. Rios, Adriana Peña Perez-Negron & Gabriel Martínez Soltero
Todos los contenidos de esta obra se comparten bajo la licencia Creative Commons Atri-bución/Reconocimiento-NoComercial-CompartirIgual 4.0 Internacional (CC BY-NC-SA 4.0). Esto implica que no está autorizado el uso comercial de la obra original ni de las eventuales obras derivadas, las cuales deberán distribuirse bajo la misma licencia que rige la obra original. No obstante, se permite a terceros compartir el contenido siempre y cuando se reconozca debidamente la autoría y la publicación original en esta editorial.
Carlos López Franco
Universidad de Guadalajara
0000-0001-8122-3799
Ingrid Palomares
Universidad de Guadalajara
Nancy Arana Daniel
Universidad de Guadalajara
0000-0002-8803-9502
Jesús Hernández Barragan
Universidad de Guadalajara
0000-0001-7518-1668
Carlos Villaseñor
Universidad de Guadalajara
0000-0003-0802-0121
Acerca de
This work presents a single neuron proportional-derivative (SNPD) control strategy for leader-follower formation control trained with the Extended Kalman Filter (EKF). SNPD trained with unregularized EKF may adapt its weights without limits, which is most significant over time which drives the formation control to instability. To address this issue, we propose to include a regularization strategy in the EKF. Since pose estimation with wheel odometry lacks precision, in this work we propose a pose estimation based on visual sensors and apriltags to improve precision with robustness against external perturbations. The effectiveness and performance of the implementations are demonstrated through real-world experiments using Turtlebot3 Waffle PI robots.
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