Robotic Scanning Mechanism for Low-Confidence Object Pose
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Solution Overview
Problem
Robotic systems lack the sophistication to duplicate human sensitivity and adaptability, particularly in executing complex tasks due to limitations in granularity of control and flexibility, leading to issues with deviations and uncertainties in real-world scenarios.
Innovation Solution
A robotic system with an enhanced scanning mechanism that derives and executes motion plans based on a confidence measure, adjusting its approach to account for uncertainties in object pose determination, allowing it to vary grip locations and motion paths to improve scanning efficiency and accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the robotic system uses a fixed motion plan based on initial pose determination, then the system structure is simple, but the scanning accuracy decreases due to pose determination errors
Solution Approach 1:
The motion plan is made dynamic by allowing the robotic system to adjust its motion path and grip location based on real-time feedback from the imaging device. The system derives alternative motion plans when pose determination errors are detected, transforming a static motion plan into an adaptive dynamic one that responds to actual object positions.
Solution Approach 2:
The system implements feedback by using the imaging device to continuously monitor object position and compare it with the initial pose determination. When deviations exceed a threshold, the system receives feedback about the error and adjusts the motion plan accordingly, creating a closed-loop control system that improves scanning accuracy.
2Measurement precision
If the robotic system adjusts motion plans based on confidence measures, then scanning accuracy improves, but the processing time increases
Solution Approach 1:
The system changes the parameter of motion plan selection based on the confidence measure of pose determination. When confidence is high, the system uses the initial motion plan without adjustment. When confidence is low, it derives alternative motion plans. This parameter-based decision-making optimizes the balance between accuracy and processing time.
Solution Approach 2:
The system applies partial adjustment by not always deriving alternative motion plans. Instead, it selectively adjusts the motion plan only when the confidence measure indicates pose determination errors are likely. This partial action approach avoids unnecessary processing time while maintaining scanning accuracy when needed.
3Adaptability or versatility
If the robotic system uses multiple alternative motion plans, then adaptability to pose errors improves, but the control complexity increases
Solution Approach 1:
The system performs preliminary action by pre-calculating alternative motion plans based on possible pose determination errors. These alternative plans are prepared in advance and stored, so when a pose error is detected, the system can quickly select from pre-prepared options rather than calculating new plans in real-time, reducing control complexity.
Solution Approach 2:
The motion planning process is segmented into distinct phases: initial pose determination, confidence assessment, alternative plan generation (if needed), and execution. This segmentation breaks down the complex control task into manageable steps, making the overall system easier to control despite the ability to handle multiple alternative plans.
Data Source
AI summary
A method for operating a robotic system including determining an initial pose of a target object based on imaging data; calculating a confidence measure associated with an accuracy of the initial pose; and determining that the confidence measure fails to satisfy a sufficiency condition; and deriving a motion plan accordingly for scanning an object identifier while transferring the target object from a start location to a task location.