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.
Arturo A. Pérez
Universidad de Guadalajara
Jesús Hernández Barragan
Universidad de Guadalajara
0000-0001-7518-1668
Javier Enrique Gómez Avila
Universidad de Guadalajara
0000-0002-9724-1729
Carlos López Franco
Universidad de Guadalajara
0000-0001-8122-3799
Nancy Arana Daniel
Universidad de Guadalajara
0000-0002-8803-9502
Acerca de
The hierarchical multitasking scheme is a useful approach for robotic systems with multiple objectives, where each objective has a lower priority than the previous one. However, some tasks can be complex enough to require significant effort for fine-tuning. In this paper, we propose the implementation of a neural incremental proportional-integrative-derivative (NI-PID) approach to control multiple tasks within a hierarchical multitasking scheme for a dual-arm robotic system. The NI-PID adapts its weights online, significantly reducing the effort required for tunning gains. The effectiveness of the proposed approach is validated through simulations on a dual-arm system modeled with the Baxter arm, featuring 7 degrees of freedom (7DOF).
Referencias
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