Paper Info
Title
Online Configuration Selection for Redundant Arrays of Inertial Sensors - Application to Robotic Systems Covered with a Multimodal Artificial Skin.
Abstract
Multiple approaches to the estimation of high-order motion derivatives for innovative control applications now rely on the data collected by redundant arrays of inertial sensors mounted on robots, with promising results. However, most of these works suffer scalability issues induced by the considerable amount of data generated by such large-scale distributed sensor systems. In this article, we propose a new adaptive sensor-selection algorithm, for distributed inertial measurements. Our approach consists in using the data of a subset of sensors, selected among a larger collection of inertial sensing elements covering a rigid robot link. The sensor selection process is formulated as an optimization problem, and solved using a projected gradient heuristics. The proposed method can run online on a robot and be used to recalculate the selected sensor arrangement on the fly when physical interaction or potential sensor failure is detected. The tests performed on a simulated UR5 industrial manipulator covered with a multimodal artificial skin, demonstrate the consistency and performance of the proposed sensor-selection algorithm.
Year
DOI
Venue
2020
10.1109/IROS45743.2020.9341453
IROS
DocType
Citations 
PageRank 
Conference
0
0.34
References 
Authors
17
3
Name
Order
Citations
PageRank
Quentin Leboutet161.46
Florian Bergner2216.14
Gordon Cheng31250115.33