On Differential Drive Robot Learning Convex Policy with Application to Path-Tracking

Author(s)

Ribeiro, A. M. ; Quiroz, C. H. C. ; Fioravanti, A. R. ; Kurka, P. R. G.

• Document type: Journal article
• Source: IFAC PAPERSONLINE; v. 54, n. 4, p. 6-pg., 2021-10-29.

Abstract

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)

• FAPESP's process: 18/05712-2 - Identification and control of a robotic electric vehicle with electronic differential
• Grantee: Alexandre Monteiro Ribeiro
• Support Opportunities: Scholarships in Brazil - Doctorate