公开(公告)号：US20250083311A1

公开(公告)日：2025-03-13

申请号：US18827825

申请日：2024-09-08

Applicant: SOUTHEAST UNIVERSITY

Inventor： Baoguo XU ,  Jiajin WANG ,  Xin WANG ,  Weifeng PENG ,  Aiguo SONG

IPC: B25J9/16

Abstract: Disclosed is an optimization modeling and robust control method for a soft robot based on a fusion prediction equation, including the following steps: deriving measurement coordinates based on the fusion prediction equation; designing an observation function based on the measurement coordinates; identifying a Koopman model based on the observation function; and designing a robust model predictive controller based on the Koopman model. Further disclosed are a fusion prediction equation and a derivation method thereof, which can derive correct, abundant but non-redundant measurement coordinates, overcoming the problem of single measurement coordinates in a soft robot system, thereby being conducive to simplifying a design process of the observation function and further improving the accuracy of the Koopman model for the soft robot.

公开(公告)号：US20250076986A1

公开(公告)日：2025-03-06

申请号：US18817208

申请日：2024-08-27

Applicant: SOUTHEAST UNIVERSITY

Inventor： Baoguo XU ,  Xin WANG ,  Jiajin WANG ,  Aiguo SONG

IPC: G06F3/01 ,  G06V10/80

Abstract: Disclosed are an affective haptic regulation system and method based on multimodal fusion, including a haptic optimal parameter adjustment module, a haptic generation module, a visual-auditory generation module, a multi-physiological signal acquisition module, a multi-sensory signal acquisition module, and a multimodal fusion emotion recognition module. The system can fuse multi-physiological signal features with audio and haptic modal features by acquiring a plurality of physiological signals of a user, accurately identify a current affective state of the user in real time through advanced data processing and analysis technology, seek for a haptic parameter with the help of an optimization theory, and achieve proactive regulation of affective state of the user; and the system can overcome the limitations of traditional subjective scale methods, effectively reduce the influence of unstable physiological signals on emotion recognition results, and significantly improve the accuracy of affective detection in the affective haptic regulation system.