Robotic Assembly Parameter Optimization via Process Categorization
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Solution Overview
Problem
The process of programming industrial robots with force control for assembly tasks has become more complex due to the dependency on interaction forces between assembled parts, making the selection of optimal parameters tedious and time-consuming, often requiring trial and error or offline analysis tools.
Innovation Solution
A system that uses a computing device to optimize robotic parameters by categorizing assembly processes, specifying search patterns, obtaining optimal parameters through techniques like Design Of Experiments (DOE), and verifying these parameters to ensure efficient assembly, independent of part sizes and using a graphical user interface for setup and optimization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If force control is used for robotic assembly, then assembly precision is improved, but programming complexity increases
Solution Approach 1:
The patent transforms the complex force control programming into a simplified parameter-based approach. By categorizing assembly processes into predetermined types and associating each type with optimized parameter sets, the system changes the control parameters from complex force profiles to simple category identifiers and parameter selections, thereby maintaining assembly precision while reducing programming complexity
Solution Approach 2:
The patent performs preliminary analysis and optimization of force control parameters offline before actual assembly operations. By pre-categorizing assembly processes and pre-determining optimal parameters for each category, the system eliminates the need for complex real-time force control programming, thus improving assembly precision through pre-optimized parameters while reducing on-site programming complexity
2Reliability
If trial and error method is used for parameter optimization, then optimal parameters can be obtained, but time consumption increases
Solution Approach 1:
The patent performs parameter optimization in advance by categorizing assembly processes and determining optimal parameters offline before actual assembly operations. This preliminary action eliminates the need for time-consuming trial and error during production, ensuring parameter reliability while dramatically reducing the time required for parameter selection
Solution Approach 2:
The patent creates a library of predetermined assembly process types with associated optimal parameters that can be copied and applied to similar assembly tasks. Instead of performing trial and error for each new assembly operation, the system copies proven parameter sets from the library, ensuring reliable parameter selection while minimizing time consumption
3Measurement precision
If offline analysis tools are used for parameter optimization, then parameter accuracy is improved, but system complexity increases
Solution Approach 1:
The patent extracts the complex offline analysis and parameter optimization functions into a separate preprocessing stage. By separating the analytical complexity from the execution system, the patent achieves high parameter accuracy through thorough offline analysis while keeping the actual robotic assembly system simple and easy to operate
Data Source
AI summary
A method and system to optimize the parameters of a robot used in an assembly process. The assembly process is categorized based on its nature which may be cylindrical, radial and multi-stage insertion. The search pattern and search parameters are specified. The parameters are optimized and the optimized parameter set are verified and when a predetermined criteria such as assembly cycle time set and/or success rate is met the optimization process stops. When the optimization stops the verified parameters are used to cause the robot to perform the categorized assembly process. If the parameters do not meet the predetermined criteria, another round of optimization using the same or other parameters can be performed.