A Lyapunov-Stable Adaptive Method to Approximate Sensorimotor Models for Sensor-Based Control

Abstract

In this article, we present a new scheme that approximates unknown sensorimotor models of robots by using feedback signals only. The formulation of the uncalibrated sensor-based regulation problem is first formulated, then, we develop a computational method that distributes the model estimation problem amongst multiple adaptive units that specialize in a local sensorimotor map. Different from traditional estimation algorithms, the proposed method requires little data to train and constrain it (the number of required data points can be analytically determined) and has rigorous stability properties (the conditions to satisfy Lyapunov stability are derived). Numerical simulations and experimental results are presented to validate the proposed method.

On Radiation-Based Thermal Servoing: New Models, Controls and Experiments

“…These results show that by continuously updating the source-object pose, the control of the individual temperature errors is not significantly affected and that ∆τ can still be asymptotically minimized. This experiment demonstrates how our new thermal servoing method can be combined with other traditional controllers (visual servoing in this case) to extend the sensorimotor capabilities of a robot [48].…”

Sensor-Based Control for Collaborative Robots: Fundamentals, Challenges, and Opportunities

“…We considered four senses: vision, touch, audition, and distance. First, we introduce a tutorial-like general formulation of sensor-based control (Navarro-Alarcon et al, 2020 ), which we instantiate for visual servoing, touch control, aural servoing, and distance-based control, while reviewing representative papers. Next, with the same formulation, we model the methods that integrate multiple sensors, while again discussing related works.…”

“…V+T (Pomares et al, 2011;Cherubini et al, 2015Cherubini et al, , 2016Chatelain et al, 2017), D+T (Navarro et al, 2014;Dean-Leon et al, 2016) Wheeled Vision (Dune et al, 2008;Tsui et al, 2011;Narayanan et al, 2016), touch (Wang et al, 2015), audition (Magassouba et al, 2015(Magassouba et al, , 2016a, V+A (Chan et al, 2012), V+T+A+D (Papageorgiou et al, 2014), D+A (Huang et al, 1999), V+D (Cherubini and Chaumette, 2013;Cherubini et al, 2014) Humanoids Touch (Bussy et al, 2012), V+T (Agravante et al, 2013(Agravante et al, , 2014 Heads Audition (Kumon et al, 2003(Kumon et al, , 2005Magassouba et al, 2016b), V+A (Okuno et al, 2001(Okuno et al, , 2004Natale et al, 2002;Hornstein et al, 2006) vision, touch, audition, and distance. First, we introduce a tutorial-like general formulation of sensor-based control (Navarro-Alarcon et al, 2020), which we instantiate for visual servoing, touch control, aural servoing, and distance-based control, while reviewing representative papers. Next, with the same formulation, we model the methods that integrate multiple sensors, while again discussing related works.…”

A Multi-Sensor Interface to Improve the Teaching and Learning Experience in Arc Welding Training Tasks

“…An automated motorized multi-sensor robot will be built to trace the welding region and the welding spot instantly, providing a better field of view during the process. The reported multi-sensor system can also be used to guide a robotic system [28] in an automatic welding application; we are currently working in this direction.…”