3D Mesh Privacy Masking for Cloud-Based Grasp Planning
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Solution Overview
Problem
Cloud-based manufacturing and robotics systems face challenges in preserving user privacy when sharing proprietary 3D geometric data, as users are reluctant to send sensitive information such as part dimensions or gear ratios, which is essential for effective grasp planning and robotic interactions.
Innovation Solution
A system that utilizes a privacy labelling tool to mask proprietary regions of a 3D object mesh, allowing only unmasked portions to be shared for grasp planning, while maintaining the privacy of sensitive areas through binary privacy labels and masking functions, ensuring that only sharable geometric properties are exposed to cloud-based services.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If users send complete 3D geometric data to cloud-based services, then grasp planning accuracy is improved, but user privacy is compromised
Solution Approach 1:
The patent segments the 3D geometric model into multiple regions with different privacy levels. Each region can be independently labeled as private or public, allowing selective sharing of geometric information. This segmentation enables the system to maintain high grasp planning accuracy for public regions while protecting sensitive private regions, thus resolving the contradiction between accuracy and privacy.
Solution Approach 2:
The patent applies local quality by assigning different privacy attributes to different regions of the same 3D model. Instead of treating the entire model uniformly, the system allows specific local regions to be marked as private or public based on their sensitivity. This enables fine-grained control over information sharing, maintaining accuracy where needed while preserving privacy where required.
2Loss of information
If users mask proprietary regions of 3D objects, then user privacy is protected, but grasp planning coverage is reduced
Solution Approach 1:
The patent introduces dynamic adjustment mechanisms that allow users to modify privacy masks based on specific grasp planning tasks. The system can dynamically adjust which regions are masked or unmasked depending on the requirements of the particular task, enabling flexible balancing between privacy protection and grasp planning coverage rather than using static masking.
Solution Approach 2:
The patent employs parameter changes by allowing users to adjust privacy mask parameters such as mask opacity, mask resolution, and mask region boundaries. These parameter adjustments enable the system to optimize the balance between privacy protection and grasp planning effectiveness for different applications and scenarios.
Data Source
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AI summary
A system for analyzing geometric properties for an object includes designing the object in a first computer process and producing information relating to the geometric properties of the object, and receiving the information in a second computer processor which identifies a first portion of the geometric property information as masked or private and second portion identified as public or shared, analysis is performed by the second processor on the public/shared portion of the geometric property information. An output based on the analysis may be provided to an industrial system performing processes on the object. A binary privacy label may be assigned to each triangle in a set of triangles representing the surfaces of the object in a 3D object mesh. The privacy label denotes an associated triangle as being private or shared. The system may be used to produce a set of planned grasps for a robotic gripper.