Robot Workpiece Skeleton Control for Irregular Shape Tasks
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Solution Overview
Problem
Existing robot systems struggle to efficiently handle irregularly shaped and flexible workpieces with individual shape differences, as they lack the ability to accurately determine task completion states and adapt to varying shapes.
Innovation Solution
A robot system that acquires workpiece images, estimates skeleton information, determines task constraints and completion states, and controls the robot to perform tasks based on this information, using a trained skeleton estimation model and functional modules for start, completion, and end state determination.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional robot systems are used to handle irregularly shaped workpieces, then the robot can perform basic picking and placing tasks, but the system cannot accurately determine task completion states or adapt to varying shapes
Solution Approach 1:
The workpiece is segmented into skeleton components (centerline, boundaries, key points) that capture essential geometric features. This segmentation allows the robot system to analyze and adapt to varying shapes by processing skeleton information rather than entire complex geometries, resolving the contradiction between adaptability and measurement precision.
Solution Approach 2:
Skeleton information serves as an intermediary representation between the raw workpiece image and the robot control system. This intermediary enables accurate determination of task completion states by providing simplified yet informative geometric descriptors that bridge the gap between visual input and control decisions.
2Manufacturing precision
If the robot system processes detailed workpiece geometry information, then task completion accuracy improves, but system complexity increases
Solution Approach 1:
The system extracts only the essential skeleton information (centerline, boundaries, key points) from the complete workpiece geometry, discarding redundant details. This extraction maintains task execution accuracy by preserving critical geometric features while significantly reducing system complexity through simplified data representation.
Solution Approach 2:
The skeleton estimation is performed in advance before robot execution, converting complex geometric processing into a preliminary step. This preliminary action simplifies subsequent control operations by providing pre-processed skeleton information, thereby reducing overall system complexity while maintaining precision.
3Adaptability or versatility
If the robot system uses skeleton estimation for task control, then the variety of processable workpieces increases, but the requirement for advanced image processing capabilities increases
Solution Approach 1:
The skeleton estimation approach provides a universal representation method that works across diverse workpiece types and shapes. This single framework enables the system to handle various geometries without requiring specialized processing for each type, increasing versatility while keeping image processing requirements manageable through a unified approach.
Data Source
AI summary
A robot system includes circuitry configured to: acquire a workpiece image showing a workpiece present in real space; estimate skeleton information indicating a skeleton of the workpiece based on the workpiece image; acquire constraint information representing a constraint regarding a task to be performed on the workpiece; determine a completion state of the task based on the acquired constraint information and the estimated skeleton information; and cause a robot placed in the real space to perform the task in accordance with the determined completion state.