Application of reinforcement learning in phase control of mid-air ultrasonic haptics
Year: 2025
Type: article
Abstract: Mid-air ultrasonic haptics is a technology that uses ultrasonic waves in air to provide tactile sensations without physical contact. To deliver a strong tactile sensation at a desired position, it is essential to create a focal point through beamforming, which involves controlling the phases of the multiple ultrasonic waves. In this study, we propose a method for deriving the optimum phase shift using reinforcement learning without the use of external equipment. The phase shifts are treated as discrete values between 0 and 360, and focal points are generated using deep Q-Network and SARSA. The evaluation results confirmed that focus was formed; however, issues were observed where the deviation from the target position increased when the phase decimation interval was increased. On the other hand, by narrowing the search range, almost 100% of the target sound pressure was achieved and highly accurate focus formation was possible.
Authors: Mifuka Nakamura, Rin Furuya, Nobuya Sato, Daisuke Mizushima
Institution: Aichi Institute of Technology
Language: English
Subfield: Cognitive Neuroscience
Field: Neuroscience
Domain: Life Sciences
Open Access status: hybrid
Estimated APC paid: $2,210
OpenAlex ID: W4409490359