Online Identification of Payload Inertial Parameters Using Ensemble Learning for Collaborative Robots
Loading...
Date
2024-01
Journal Title
Journal ISSN
Volume Title
Type
Article
Publisher
Institute of Electrical and Electronics Engineers Inc
Series Info
IEEE ROBOTICS AND AUTOMATION LETTERS,;VOL. 9, NO. 2, FEBRUARY 2024
Scientific Journal Rankings
Abstract
Collaborative robots (Cobots) are essential in flexible
automation solutions, enabling fast and easy reconfiguration to
adapt to varying task requirements in dynamic environments.
This requires the ability to safely handle different payloads with
varying inertial parameters, which may not be known in advance.
Hence, online identification of the payload’s inertial parameters
becomes essential for safe interactions, accurate path following,
and stable grasping. Most existing methods require additional sensors, calibration procedures, or custom filtering, which increases
the complexity and estimation time. In this letter, we propose a
novel online identification method that employs a bagging ensemble machine learning approach to identify the payload inertial
parameters without external sensors or additional filtering and
calibration steps. The method uses available joint position, velocity, and torque measurements from the Cobot to train neural
networks and decision trees as weak learners. The method is tested
in simulation and validated using the Franka Emika Panda Cobot.
The results showed that our method outperforms the state-ofthe-art recursive least square methods reducing prediction errors
by 75%–78% for mass and 49.5%–60% for the center of mass,
while estimating accurate payload parameters within the first time
step.
Description
Keywords
In-Hand manipulation; online identification; payload dynamics, Engineering controlled terms Collaborative robots; Decision trees; Learning systems; Least squares approximations; Parameter estimation Engineering uncontrolled terms Collaborative robots; Ensemble learning; Flexible automation; Hand manipulation; In-hand manipulation; Inertial parameters; On-line identification; Payload; Payload dynamics; Task analysis Engineering main heading Calibration