Robotic Scanning Motion Planning Under Pose Uncertainty
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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 factors.
Innovation Solution
A robotic system with an enhanced scanning mechanism that derives and executes motion plans based on a confidence measure, allowing for adjustments in grip location, pose, and motion path to account for uncertainties, using imaging devices and sensors to identify object poses and orientations with confidence calculations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If traditional robotic systems execute tasks with fixed control parameters, then device complexity is reduced, but task execution accuracy deteriorates due to inability to account for real-world uncertainties
Solution Approach 1:
The system performs preliminary actions by calculating multiple candidate motion plans before task execution, each plan accounting for different uncertainty scenarios. The robot prepares grip forces, approach vectors, and motion parameters in advance, selecting the optimal plan based on predicted outcomes rather than reacting to deviations during execution.
Solution Approach 2:
The control system transitions from static fixed parameters to dynamic adaptive parameters. Motion plans are generated with variable grip forces, adjustable approach vectors, and flexible timing parameters that can be optimized based on object properties and environmental conditions, allowing the system to adapt to real-world uncertainties.
2Adaptability or versatility
If robotic systems use simplified motion planning, then device complexity is reduced, but adaptability to different conditions deteriorates
Solution Approach 1:
The motion planning process is segmented into distinct phases: approach phase, gripping phase, and execution phase. Each phase has its own set of candidate plans with specific parameters optimized for that phase. This segmentation allows the system to manage complexity by breaking down the overall planning task into smaller, more manageable components.
Solution Approach 2:
The system employs parameter changes by varying grip forces, approach vectors, and motion timing across different candidate plans. By systematically changing these parameters to account for different uncertainty scenarios, the system achieves high adaptability without requiring a fundamentally complex planning architecture.
3Reliability
If robotic systems execute tasks without confidence measures, then device complexity is reduced, but task reliability deteriorates due to unaccounted deviations
Solution Approach 1:
The system implements feedback by calculating confidence measures for each candidate motion plan based on object detection data, environmental sensors, and task requirements. This confidence information feeds back into the plan selection process, allowing the system to choose the most reliable plan while automatically accounting for uncertainties without requiring external oversight.
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.