Robot Cell Layout Planning for Multi-Model Assembly
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Solution Overview
Problem
Planning the arrangement of a robot cell for different product models is inefficient due to the difficulty in determining the position and orientation of a 6-axis arm robot, requiring significant manual adjustments and specialized facilities, which hinders quick response to manufacturing changes.
Innovation Solution
A planning device that searches for optimal positions and orientations of a robot and fixture using a three-dimensional database, generates disassembly and assembly task sequences, and calculates trajectories to improve the efficiency of robot cell arrangement and assembly work.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If manual planning is used to determine robot position and orientation for different product models, then the robot cell can be customized for each product, but the preparation time and manual adjustment work increase significantly
Solution Approach 1:
The patent uses virtual three-dimensional data models to create digital copies of the robot, workpiece, and surrounding facilities. These virtual models allow for automated planning and simulation of robot cell arrangements without physical prototypes or manual trial-and-error adjustments, significantly reducing preparation time while maintaining adaptability to different product models.
Solution Approach 2:
The patent replaces manual mechanical planning and physical adjustment with automated computer-based algorithms. The system automatically calculates optimal robot positions, orientations, and trajectories by processing virtual three-dimensional data, substituting human expertise and manual trial-and-error with computational intelligence.
2Manufacturing precision
If manual adjustment is used to physically position devices for different product models, then the robot cell can be optimized for each product, but the number of manual adjustments and specialized facilities required increases
Solution Approach 1:
The patent creates virtual copies of all physical components including the robot, workpiece, fixture, and surrounding facilities. These digital twins allow for precise positioning calculations and simulation before actual implementation, achieving high positioning precision without repeated manual adjustments or specialized facilities for each product model.
Solution Approach 2:
The patent develops a universal automated planning system that can handle multiple product models and robot cell configurations through a single integrated software platform. This multi-functional system eliminates the need for specialized facilities or separate planning processes for different products, improving ease of operation while maintaining precision.
3Productivity
If automated planning is implemented using virtual three-dimensional data, then the preparation efficiency improves, but the complexity of the planning system increases
Solution Approach 1:
The patent utilizes existing virtual three-dimensional data from CAD systems and facility management systems, rather than creating new complex planning software from scratch. By leveraging these existing digital models, the system achieves high preparation efficiency while minimizing the complexity of the planning system itself.
Solution Approach 2:
The patent introduces a database as an intermediary layer between the virtual three-dimensional data sources and the planning algorithms. This database standardizes and organizes the data, simplifying the planning system's interface requirements and reducing overall system complexity while maintaining high productivity through automated processing.
Data Source
AI summary
In an arrangement design of a robot cell and of robot work, a planning device searches an arrangement graph, that shows candidates for relative positions and orientations between a robot and a fixture; searches a disassembly task sequence, of disassembly tasks that are operations of the robot for disassembling components from the assembly in an arrangement of the relative positions and orientations between the robot and the fixture, and searches for, based on a point sequence of positioning relay points of the robot, a trajectory in which the robot disassembles the components from the assembly; and generates assembly tasks formed of a point sequence obtained by reversing the point sequence of the relay points, an assembly trajectory of the robot which is reverse to a disassembly trajectory formed of interpolation points between the point sequences, and an assembly sequence, which is a reverse order of the disassembly task sequence.