Electropneumatic Oscillators Using Nonlinear Inflatables

Animals and robots employ central pattern generators, networks that invoke rhythmic patterns from constant inputs, to orchestrate limb movements during locomotion. Artificial central pattern generators (CPGs) can be either implemented in software or constructed in a physical domain. The former lacks embodiment, inhibiting direct interactions with the physical world. The latter is restricted by a complex translation of abstract functions to the physical domain, e.g., negative feedback, if‐then behavior, etc. Here, self‐oscillators, a rudimentary type of central pattern generators, that find ground in both the pneumatic and electrical domains are demonstrated. First, the hysteretic behavior of previously developed conical membranes is analyzed in deformation space. This state‐space behavior is then transformed to the electrical domain by means of a stretchable strain sensor. Next, an analog comparator distinguishes between the two states and instructs a pneumatic solenoid valve to counteract the current state. As a result, a stable oscillation emerges, with a frequency that is dominated by the physical characteristic of the pneumatic circuit. As such, the proposed electropneumatic oscillator provides a promising platform for building complex CPGs that control interactive neuromorphic robots.

Authors

Institutions

Publication Details

Journal
Advanced Intelligent Systems
Published
2025-02-05
DOI
https://doi.org/10.1002/aisy.202400695
Citations
2
Primary Topic
Advanced MEMS and NEMS Technologies
Type
article
Field-Weighted Citation Impact
1.46

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Electropneumatic Oscillators Using Nonlinear Inflatables

Edoardo Milana, Benjamin Gorissen, Lorenzo Migliorini, Elias De Smet et al.
2 citations
Advanced Intelligent Systems
Advanced MEMS and NEMS Technologies
1.46
article

Electropneumatic Oscillators Using Nonlinear Inflatables

Edoardo Milana, Benjamin Gorissen, Lorenzo Migliorini, Elias De Smet, P. Milani
article en
2 citations

Abstract

Animals and robots employ central pattern generators, networks that invoke rhythmic patterns from constant inputs, to orchestrate limb movements during locomotion. Artificial central pattern generators (CPGs) can be either implemented in software or constructed in a physical domain. The former lacks embodiment, inhibiting direct interactions with the physical world. The latter is restricted by a complex translation of abstract functions to the physical domain, e.g., negative feedback, if‐then behavior, etc. Here, self‐oscillators, a rudimentary type of central pattern generators, that find ground in both the pneumatic and electrical domains are demonstrated. First, the hysteretic behavior of previously developed conical membranes is analyzed in deformation space. This state‐space behavior is then transformed to the electrical domain by means of a stretchable strain sensor. Next, an analog comparator distinguishes between the two states and instructs a pneumatic solenoid valve to counteract the current state. As a result, a stable oscillation emerges, with a frequency that is dominated by the physical characteristic of the pneumatic circuit. As such, the proposed electropneumatic oscillator provides a promising platform for building complex CPGs that control interactive neuromorphic robots.

Advanced Intelligent SystemsVol. 7(6)
University of Freiburg (DE), University of Milan (IT), Flanders Make (Belgium) (BE), KU Leuven (BE)
European Commission, Deutsche Forschungsgemeinschaft, Fonds Wetenschappelijk Onderzoek, Vlaamse regering, European Research Council
Openalex Percentile: Top 20%
Advanced MEMS and NEMS Technologies
1.46
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.