Robotic Scanning Mechanism for Low-Confidence Object Poses
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Solution Overview
Problem
Existing robotic systems lack the sophistication to duplicate human sensitivity and adaptability, particularly in executing complex tasks that involve deviations or uncertainties from real-world factors.
Innovation Solution
A robotic system with an enhanced scanning mechanism that derives and executes motion plans based on uncertainties associated with initial poses of objects, using imaging devices to identify object locations and poses, and calculating a confidence measure to adjust the motion plan accordingly.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional robotic systems execute tasks based on predetermined motion plans, then task execution is simple and fast, but accuracy deteriorates when pose determination errors or uncertainties occur
Solution Approach 1:
The system continuously monitors the actual pose of objects during task execution and compares it with the predetermined motion plan. When deviations exceed a threshold, the system automatically generates corrective motion plans to maintain accuracy. This feedback mechanism resolves the contradiction by adding intelligence that adapts to uncertainties without requiring complete system redesign.
Solution Approach 2:
The motion plan transitions from static and predetermined to dynamic and adaptive. The system can modify motion parameters in real-time based on detected pose errors, allowing the robotic system to maintain reliability under uncertain conditions while managing complexity through selective adaptation rather than complete dynamic reconfiguration.
2Reliability
If the robotic system accounts for uncertainties and pose determination errors, then task execution accuracy improves, but processing time increases
Solution Approach 1:
The system applies uncertainty compensation selectively rather than universally. It monitors pose determination confidence levels and only activates corrective motion planning when errors exceed acceptable thresholds. This partial action approach maintains accuracy when needed while avoiding unnecessary processing delays during confident operations.
Solution Approach 2:
The robotic system autonomously detects and corrects its own pose determination errors without requiring external intervention or extensive recalculation. By using the confidence metrics from its own sensing systems and automatically adjusting motion plans, the system maintains accuracy while minimizing additional processing time.
3Adaptability or versatility
If the robotic system uses confidence metrics to adjust motion plans, then adaptability improves, but computational complexity increases
Solution Approach 1:
The system applies different levels of adaptability to different aspects of motion planning based on local confidence metrics. High-confidence regions use simple predetermined plans, while low-confidence regions trigger more complex adaptive planning. This local differentiation provides adaptability where needed while maintaining simplicity elsewhere, reducing overall computational burden.
Solution Approach 2:
The system adjusts motion plan parameters dynamically based on confidence metrics rather than completely redesigning motion plans. By modifying specific parameters such as velocity, acceleration, or path points only when confidence is low, the system achieves adaptability with minimal computational overhead compared to full re-planning.
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.