Robotic Assembly Force Control Tuning Under Pose Uncertainty
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Solution Overview
Problem
Current robotic assembly systems face challenges in tuning force control parameters for general assembly tasks due to the need for manual trial and error, which is time-consuming, expensive, and potentially dangerous, and existing simulation-based systems are limited in their applicability and require significant human expertise.
Innovation Solution
A method using numerical optimization and closed-loop force control simulation to autonomously tune force control parameters in a simulation environment, where random samples of parameter values are evaluated, and the optimization routine iteratively refines the parameter distribution to find optimal values, accounting for uncertainties such as fixture pose errors, and these parameters are then applied to real-world robotic assembly operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual tuning of force control parameters is performed in a real robotic system, then the assembly task can be adapted to specific physical conditions, but the process is time-consuming and costly
Solution Approach 1:
The patent creates a digital twin or simulation model of the robotic assembly system that replicates the physical system's behavior. Force control parameters are tuned in this virtual copy through automated optimization algorithms, eliminating the need for time-consuming manual trial-and-error tuning on the actual robotic system. The optimized parameters from the simulation are then transferred to the real system, achieving high assembly success rates without the time loss associated with physical tuning.
Solution Approach 2:
The patent performs force control parameter optimization in advance through simulation before deploying the robotic system for actual assembly tasks. By pre-tuning the parameters in a virtual environment using automated algorithms, the system准备好 optimal control settings that can be directly applied to the real robotic system, significantly reducing the time required for on-site parameter adjustment and improving assembly reliability.
2Productivity
If existing simulation-based parameter tuning systems are used, then parameter optimization can be performed in advance, but these systems require significant human expertise and are limited to specific assembly task types
Solution Approach 1:
The patent develops a universal simulation-based parameter tuning system that can handle multiple types of assembly tasks through a standardized interface and generalized optimization framework. The system uses a modular architecture where the core optimization algorithm remains the same across different task types, requiring minimal configuration and no specialized human expertise for each new application. This universality enables automated parameter tuning for various assembly operations while reducing system setup complexity.
Solution Approach 2:
The patent implements an automated optimization routine that autonomously tunes force control parameters without requiring human operator intervention or expertise. The system self-configures by automatically generating simulation models, selecting appropriate parameter ranges, executing optimization algorithms, and transferring results to the robotic system. This self-service capability eliminates the need for highly skilled operators and simplifies the overall system complexity while maintaining high tuning efficiency.
3Ease of manufacture
If force control parameters are tuned without considering pose uncertainties, then the tuning process is simpler and faster, but the assembly performance deteriorates under real-world conditions
Solution Approach 1:
The patent incorporates pose uncertainties into the simulation model and uses feedback from multiple simulated trials with varying uncertainty conditions to optimize force control parameters. The optimization algorithm receives feedback on assembly success rates across different uncertainty scenarios and adjusts parameters accordingly. This feedback-driven approach maintains ease of automated tuning while significantly improving robustness to real-world pose uncertainties, as the parameters are specifically optimized to handle such variations.
Solution Approach 2:
The patent prepares the robotic system for real-world uncertainties by pre-training the force control parameters in simulation environments that explicitly model pose errors and variations. By exposing the optimization process to these uncertainties beforehand, the system develops compensatory control strategies that cushion against the impact of real-world deviations. This prior cushioning approach maintains tuning simplicity through automation while ensuring reliable assembly performance despite pose uncertainties during actual operation.
Data Source
AI summary
A method for tuning the force control parameters for a general robotic assembly operation. The method uses numerical optimization to evaluate different combinations of the parameters for a robot force controller in a simulation environment that is built based on a real-world robotic setup. This method performs autonomous tuning for assembly tasks based on closed loop force control simulation, where random samples from a distribution of force control parameter values are evaluated, and the optimization routine iteratively redefines the parameter distribution to find optimal values of the parameters. Each simulated assembly is evaluated using multiple simulations including random part positioning uncertainties. The performance of each simulated assembly is evaluated by the average of the simulation results, thus ensuring that the selected control parameters will perform well in most possible conditions. Once the parameters have been optimized, they are applied to real robots to perform the actual assembly operation.