Current-Based Impedance Control for Interacting with Mobile Manipulators

As robots shift from industrial to human-centered spaces, adopting mobile manipulators, which expand workspace capabilities, becomes crucial. In these settings, seamless interaction with humans necessitates compliant control. Two common methods for safe interaction, admittance, and impedance control, require force or torque sensors, often absent in lower-cost or lightweight robots. This paper presents an adaption of impedance control that can be used on current-controlled robots without the use of force or torque sensors and shows its application for compliant control of a mobile manipulator. A calibration method is designed that enables estimation of the actuators’ current/torque ratios and frictions, used by the adapted impedance controller, and that can handle model errors. The calibration method and the performance of the designed controller are experimentally validated using the Kinova GEN3 Lite arm. Results show that the calibration method is consistent and that the designed controller for the arm is compliant while also being able to track targets with five-millimeter precision when no interaction is present. Additionally, this paper presents two operational modes for interacting with the mobile manipulator: one for guiding the robot around the workspace through interacting with the arm and another for executing a tracking task, both maintaining compliance to external forces. These operational modes were tested in real-world experiments, affirming their practical applicability and effectiveness.Code: https://github.com/tud-amr/mobile-manipulator-compliance

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Publication Details

Published
2024-10-14
DOI
https://doi.org/10.1109/iros58592.2024.10802856
Citations
2
Primary Topic
Context-Aware Activity Recognition Systems
Type
article
Field-Weighted Citation Impact
0.50
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article

Current-Based Impedance Control for Interacting with Mobile Manipulators

Javier Alonso–Mora, Gianluca Garofalo, Luzia Knoedler, Jelmer de Wolde
2 citations
Context-Aware Activity Recognition Systems
0.50
article

Current-Based Impedance Control for Interacting with Mobile Manipulators

Javier Alonso–Mora, Gianluca Garofalo, Luzia Knoedler, Jelmer de Wolde
article en
2 citations

Abstract

As robots shift from industrial to human-centered spaces, adopting mobile manipulators, which expand workspace capabilities, becomes crucial. In these settings, seamless interaction with humans necessitates compliant control. Two common methods for safe interaction, admittance, and impedance control, require force or torque sensors, often absent in lower-cost or lightweight robots. This paper presents an adaption of impedance control that can be used on current-controlled robots without the use of force or torque sensors and shows its application for compliant control of a mobile manipulator. A calibration method is designed that enables estimation of the actuators’ current/torque ratios and frictions, used by the adapted impedance controller, and that can handle model errors. The calibration method and the performance of the designed controller are experimentally validated using the Kinova GEN3 Lite arm. Results show that the calibration method is consistent and that the designed controller for the arm is compliant while also being able to track targets with five-millimeter precision when no interaction is present. Additionally, this paper presents two operational modes for interacting with the mobile manipulator: one for guiding the robot around the workspace through interacting with the arm and another for executing a tracking task, both maintaining compliance to external forces. These operational modes were tested in real-world experiments, affirming their practical applicability and effectiveness.Code: https://github.com/tud-amr/mobile-manipulator-compliance

Delft University of Technology (NL)
Openalex Percentile: Top 32%
Context-Aware Activity Recognition Systems
0.50
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Current-Based Impedance Control for Interacting with Mobile Manipulators — Javier Alonso–Mora, Gianluca Garofalo, et al. · (2024) | TGRS Research Map | TGRS