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Proposal of an Embodied Airbag-Pressure-Based Control Interface for Inflatable Personal Mobility Devices

Reon Hayami, Bill Falk, Takuya Sasatani, Shigeki Sugano, Mitsuhiro Kamezaki

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Key figure (auto-extracted from paper)
An inflatable personal mobility device can be directly controlled through its own body by interpreting operator pressing, leaning, and pushing inputs via internal airbag pressure changes.
inflatable mobility embodied control air pressure interface personal mobility devices soft robotics human-machine interface

Problem

Existing personal mobility device interfaces rely on external add-ons like joysticks or handlebars, requiring separate attachment, user-specific adaptation, and additional learning effort.

Approach

The system uses the PMD’s inflatable airbags as sensing elements to detect operator body movements, dynamically adjusting internal pressure based on estimated weight to translate these inputs into speed commands.

Key results

  • Recognized press, lean, and double-push inputs through airbag pressure fluctuations
  • Implemented weight-adaptive pressure adjustment to minimize required input force
  • Achieved stable translational and angular speed control via embodied movements
  • Mitigated terrain-induced pressure noise with filter processing for reliable operation

Why it matters

Provides an intuitive, integrated control paradigm for soft mobility devices that reduces setup barriers and enhances accessibility for diverse users.

Abstract

The demand for personal mobility devices (PMDs) has increased, prompting studies on various control interfaces such as joysticks and handlebars. However, these interfaces re- main external to the PMD, and no system has been developed in which the PMD itself functions as the interface. This study pro- poses an interface that measures and controls the internal air pressure of an inflatable PMD, enabling the system to recognize operator inputs, such as pressing, leaning, or pushing, directly from the PMD body. The system adjusts air pressure according to the operator’s estimated weight, allowing operation with min- imal force, while continuous speed control is realized through press, lean, and double-push inputs. Experimental results demon- strated that translational and angular speeds could be controlled through embodied body movements, and filter processing effec- tively mitigated the influence of air pressure fluctuations caused by uneven terrain, ensuring stable operation. Tests conducted on indoor and outdoor courses, including obstacles and uneven sur- faces, showed operability comparable to a joystick, though nar- rower paths required more time to navigate. This study contrib- utes a novel embodied air-pressure-based control paradigm that directly integrates the interface into the PMD itself.

Index terms

Soft Robot Applications Modeling Control and Learning for Soft Robots Physical Human-Robot Interaction

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