| Texto completo | |
| Autor(es): |
Ribeiro, A. M.
;
Quiroz, C. H. C.
;
Fioravanti, A. R.
;
Kurka, P. R. G.
Número total de Autores: 4
|
| Tipo de documento: | Artigo Científico |
| Fonte: | IFAC PAPERSONLINE; v. 54, n. 4, p. 6-pg., 2021-10-29. |
| Resumo | |
This paper presents an experimental validation of a learning convex policy for path-tracking on a differential drive robot. An online implementation of the convex control policy (COCP) is provided in the ROS environment using the CVXGEN package that runs on the on-board computer in a real-time application. The control policies are trained in an off-board computer considering a stochastic kinematic description of the robot and using approximate gradient method for a given cost-to-go metric function. The policy is validated through simulation and experimental evaluation. In addition, to certify the training efficacy, the experiment is also evaluated using the untuned policy. A discussion regarding trajectory errors is presented as well as final considerations for the solver and real-time concerns. Copyright (C) 2021 The Authors. (AU) | |
| Processo FAPESP: | 18/05712-2 - Identificação e controle de um veículo elétrico robótico com diferencial eletrônico |
| Beneficiário: | Alexandre Monteiro Ribeiro |
| Modalidade de apoio: | Bolsas no Brasil - Doutorado |