Robotic Limb Pose Tracking via Computer Vision
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Solution Overview
Problem
Robotic systems designed for consumer use face challenges in achieving accuracy and precision due to the use of lighter, cheaper components that can become less accurate over time, leading to compounded inaccuracies in mechanical components and spatial errors at the robotic limb's end-effector, especially in home environments where tasks require lower precision and aesthetics.
Innovation Solution
A computer vision system that combines lightweight and low-cost components with sensor data from image sensors to increase accuracy and precision by tracking and adjusting the pose of robotic limbs, using methods like 3D segmentation networks and machine learning to classify objects and determine current and goal poses, thereby compensating for mechanical imperfections and ensuring accurate object interaction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If lightweight and low-cost components are used in robotic systems, then cost and accessibility are improved, but accuracy and precision deteriorate due to mechanical imperfections and wear
Solution Approach 1:
The patent replaces mechanical precision components with a vision-based sensing and control system. Image sensors capture visual data of the robotic limb's position, and computer processing determines the actual pose, substituting expensive mechanical encoders and precision sensors with affordable cameras and algorithms.
Solution Approach 2:
The system continuously captures image data of the robotic limb's position, compares the actual pose with the commanded pose, and uses this feedback to compensate for mechanical inaccuracies. This closed-loop vision-based feedback mechanism allows low-cost components to achieve high positioning accuracy.
2Device complexity
If mechanical components are used without compensation, then device complexity is reduced, but reliability deteriorates over time due to wear and accumulated inaccuracies
Solution Approach 1:
Instead of using complex mechanical compensation mechanisms or high-precision components that wear, the patent employs a vision-based system that digitally tracks and compensates for positional drift, eliminating mechanical complexity while improving long-term reliability.
Solution Approach 2:
The robotic system performs self-correction by continuously monitoring its own position through image sensors and automatically adjusting based on the visual feedback, enabling it to compensate for wear and mechanical imperfections without external intervention or complex maintenance.
3Manufacturing precision
If vision-based pose tracking is implemented, then positioning accuracy is improved, but device complexity increases due to additional sensors and processing
Solution Approach 1:
The system creates a visual copy (image) of the robotic limb's physical position and uses this digital representation to determine pose accuracy. By working with image data rather than adding physical sensors to the limb, the system achieves high positioning accuracy without increasing mechanical complexity.
Solution Approach 2:
The patent introduces image sensors as an intermediary between the robotic limb and the control system. Rather than directly measuring physical quantities with complex sensors, the system uses visual imaging as an intermediate step to infer position and orientation, simplifying the overall measurement architecture.
4Manufacturing precision
If high-precision mechanical components are used, then initial accuracy is improved, but cost increases and maintenance requirements increase
Solution Approach 1:
The patent substitutes expensive high-precision mechanical components with affordable image sensors and computational algorithms. The vision system achieves and maintains positioning accuracy without requiring costly precision-machined components, reducing both initial cost and maintenance expenses.
Solution Approach 2:
The system changes the measurement parameter from direct mechanical position sensing to visual position estimation. By capturing images and computationally determining pose from visual data, the system achieves high accuracy with low-cost components, avoiding the need for expensive mechanical precision.
Data Source
AI summary
A method includes accessing RGB and depth image data representing a scene that includes at least a portion of a robotic limb. Using this data, a computing system may segment the image data to isolate and identify at least a portion of the robotic limb within the scene. The computing system can determine a current pose of the robotic limb within the scene based on the image data, joint data, or a 3D virtual model of the robotic limb. The computing system may then determine a desired goal pose, which may be based on the image data or the 3D virtual model. Based on the determined goal pose, the computing device determines the difference between the current pose and the goal pose of the robotic limb, and using this difference, provides a pose adjustment that for the robotic limb.