Robotic Assembly Parameter Optimization via Automated Search Patterns
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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 parts, making the selection of optimal parameters tedious and time-consuming, often requiring trial and error or offline analysis tools.
Innovation Solution
A computer program product that categorizes assembly processes, specifies search patterns and parameters, obtains optimal parameters using techniques like Design Of Experiment (DOE), and verifies these parameters to control robots for efficient assembly, independent of part sizes and characteristics, thereby streamlining the robotic assembly parameter optimization process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If force control is used for robotic assembly tasks, then the robot can perform tight-tolerance assembly tasks that cannot be performed by conventional position control, but the programming process becomes more difficult and parameter selection becomes tedious and time-consuming
Solution Approach 1:
The patent applies preliminary action by pre-defining multiple assembly process types (e.g., insertion, attachment, fastening) with associated standard search patterns and parameter ranges. Before actual assembly execution, the system categorizes the specific assembly task into one of these predefined types, automatically selecting appropriate search patterns and parameter settings. This eliminates the need for operators to manually tune force control parameters through trial and error, significantly reducing programming complexity while maintaining the precision benefits of force control.
2Reliability
If optimal parameters are obtained by trial and error or offline analysis tools, then the robot can achieve reliable assembly performance, but the process is tedious and time-consuming
Solution Approach 1:
The patent implements self-service by enabling the robot controller to automatically optimize assembly parameters during the assembly process itself. The system continuously monitors actual assembly outcomes and force control data, then autonomously adjusts search pattern parameters and force control settings for subsequent assembly operations. This real-time self-optimization eliminates the need for time-consuming offline analysis and trial-and-error tuning, achieving high assembly success rates while dramatically reducing parameter optimization time.
3Reliability
If the robot performs search patterns to locate the receiving part, then the assembly can proceed when contact is made, but the assembly cycle time increases due to the search process
Solution Approach 1:
The patent applies dynamics by implementing adaptive search patterns that dynamically adjust their characteristics based on real-time feedback. The system modifies search velocity, search range, and search intensity according to the specific assembly context and detected part characteristics. This dynamic adaptation allows the robot to perform efficient searches that minimize cycle time while maintaining reliable contact detection, avoiding both overly aggressive searches that miss parts and overly conservative searches that waste time.
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.