Point cloud driven arm-hand operating system

By using a grouped point cloud-driven arm-hand operating system, combined with virtual reality glasses and a dual-layer feature fusion diffusion denoising algorithm, the problems of slow response speed and poor perception ability of traditional arm-hand operating systems in complex scenarios are solved, enabling rapid deployment and high-precision environmental perception.

CN120949936BActive Publication Date: 2026-05-26HARBIN INST OF TECH
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Patent Information

Application Number
CN202511072437.0
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2025-08-01
Publication Date
2026-05-26
Estimated Expiration
2045-08-01

AI Technical Summary

Technical Problem

Traditional arm and hand operating systems are slow to respond and have poor perception capabilities in complex scenarios, especially in unstructured environments where it is difficult to balance the technical contradiction between real-time response and rapid deployment.

Method used

An arm-hand operating system driven by grouped point clouds is adopted. Combined with operation data collected by virtual reality glasses, a grouped point cloud encoder and a two-layer feature fusion diffusion denoising algorithm are designed to achieve rapid perception and real-time response.

Benefits of technology

The system deployment time is reduced by 35%, the response speed is improved by 60%, the environmental perception accuracy reaches 80%, the inverse kinematics calculation frequency can reach 100Hz, and the accuracy exceeds 90%.

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Abstract

This invention discloses a grouped point cloud-driven arm-hand operating system. The arm-hand operating system includes a VR-based teleoperation data acquisition module, an environmental perception and encoding unit, and a two-layer feature fusion diffusion denoising model. Specifically: the VR-based teleoperation data acquisition module consists of a posture recognizer, a posture mapper, and a data storage module; the environmental perception and encoding unit consists of a point cloud grouper, a point cloud encoding module, and a state integrator; and the two-layer feature fusion diffusion denoising model consists of a two-layer feature fusion module and a diffusion denoising network. This arm-hand operating system can respond to environmental changes in real time, achieving comprehensive scene perception while quickly formulating task execution plans. This data acquisition-algorithm deployment collaborative framework effectively solves the challenge of balancing cost, speed, and accuracy in intelligent arm-hand operation, providing a cost-effective solution for embodied intelligence, robotic operations, and other fields.
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