Robot Pouring Region Identification via Hierarchical Connectivity Search
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Solution Overview
Problem
Existing methods struggle to enable robots to autonomously select and optimize pouring regions, limiting their ability to complete pouring tasks accurately and adaptively across various scenarios.
Innovation Solution
The method employs hierarchical processing and connectivity maximization to identify optimal pouring regions by generating trajectory and path plans, acquiring image information, and searching for maximum intersection points between source and target containers, allowing for precise identification and optimization of pouring regions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If a fixed pouring region is given to the robot, then the robot can complete pouring tasks with simple control, but the robot cannot autonomously select and optimize the pouring region for different scenarios
Solution Approach 1:
The pouring region identification is divided into two hierarchical stages: rough identification (determining target container spatial region and generating trajectory plans) and fine identification (obtaining precise pouring region through connectivity maximization). This segmentation allows the system to achieve high automation by breaking down the complex task into manageable sub-tasks that can be executed sequentially with appropriate complexity at each level.
Solution Approach 2:
The rough identification stage performs preliminary actions by locating the target container and generating trajectory plans before the fine identification stage. This preliminary action reduces the search space for the subsequent fine identification, enabling autonomous optimization without requiring the full complexity to be present in every stage.
2Measurement precision
If hierarchical processing with connectivity maximization is implemented, then the robot can autonomously identify and optimize pouring regions accurately, but the computational complexity and processing time increase
Solution Approach 1:
The identification process is segmented into rough and fine stages, where the rough stage quickly narrows down the search space to a specific region, and the fine stage then performs connectivity maximization only within this reduced space. This segmentation maintains high precision while reducing overall computation time compared to performing full connectivity maximization across the entire workspace.
Solution Approach 2:
The rough identification performs preliminary actions to constrain the search space before fine identification begins. By pre-determining the spatial region and generating trajectory plans, the system reduces the computational burden of the subsequent connectivity maximization, achieving a balance between precision and time efficiency.
3Adaptability or versatility
If the robot is equipped with autonomous pouring region identification capability, then the robot can adapt to diverse pouring scenarios, but the system complexity and difficulty of implementation increase
Solution Approach 1:
The system segments the adaptation challenge into two layers: the rough identification layer handles scenario-specific variations (different containers, positions, orientations) through flexible spatial region determination, while the fine identification layer applies a universal connectivity maximization algorithm. This segmentation enables adaptability without requiring complete redesign of the entire system for each scenario.
Solution Approach 2:
The connectivity maximization algorithm serves as a universal component that can be applied across different pouring scenarios once the search space is constrained. The same fine identification methodology works for various container types, liquids, and pouring conditions, providing versatility without proportionally increasing system complexity.
Data Source
AI summary
An optimized method for identifying robot pouring regions based on hierarchical processing and connectivity maximization and a system thereof, wherein the optimized method includes: rough identification stage: identifying and positioning, according to working characteristics of mobile robot, target container through vision module when a distance from target region is relatively far, and generating information collection position of target container required for fine identification according to the positioning; fine identification stage: obtaining, by mobile robot, information of target container and source container, and generating corresponding connectable domains, then identifying and optimizing robot pouring region by using connectivity maximization method. It solves problems of autonomous identification of pouring region of the mobile robot, can be applied to most target containers and source containers, and enhances the generalization ability of robot pouring manipulation skills.