Adjustable interaction control using genetic algorithm for enhanced coupled dynamics in tool-part contact

Author(s)

Lahr, Gustavo J. G. ; Garcia, Henrique B. ; Savazzi, Jose O. ; Moretti, Caio B. ; Aroca, Rafael V. ; Pedro, Leonardo M. ; Barbosa, Gustavo F. ; Caurin, Glauco A. P. ; Bicchi, A ; Okamura, A

• Document type: Journal article
• Source: 2017 IEEE/RSJ INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS (IROS); v. N/A, p. 6-pg., 2017-01-01.

Abstract

Impedance control is commonly implemented for robotic contact applications. Its performance is a function of the dynamic coupling between the environment and the robot's impedance controller: inertia, stiffness and damping. An interaction task may be considered successfully accomplished when the elected performance criteria, such as rise time, overshoot and accommodation are achieved, all in addition to guaranteeing stability. The selection and online changes of impedance parameters for such purpose is considered challenging for practical applications, given the non modeled dynamics of the robot and its intrinsic impedance, uncertainties in the dynamics of environment and tool. This study proposes the usage of a multi-objective genetic algorithm to obtain enhanced impedance controller gains, with the purpose of providing impedance control adaptability to a robot in a real physical interaction application. In this context, a one-degree-of-freedom mechanical contact model is proposed to obtain the values of the objective function. A genetic algorithm is used to obtain the best gains, as this is a nonlinear task. Simulation and experimental results are presented and discussed validating the system performance, which has shown a convergence of less than 6 generations. Tendencies to convergence of controller parameters were observed and discussed. (AU)

• FAPESP's process: 15/07484-9 - Real-time movement synthesis for dynamic robot precision assembly task
• Grantee: Gustavo Jose Giardini Lahr
• Support Opportunities: Scholarships in Brazil - Master
• FAPESP's process: 15/24343-0 - Comparative analysis between machine learning reinforcement methods applied in aeronautics furniture production
• Grantee: João Victor Rocha Soares
• Support Opportunities: Scholarships in Brazil - Scientific Initiation
• FAPESP's process: 13/07276-1 - CEPOF - Optics and Photonic Research Center
• Grantee: Vanderlei Salvador Bagnato
• Support Opportunities: Research Grants - Research, Innovation and Dissemination Centers - RIDC