Robot Control Program Optimization in Physics-Based Simulation
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Solution Overview
Problem
Current simulation frameworks for robots are inadequate for testing processes involving high mechanical interaction with real environments, as they lack detailed geometric models and real-time sensor data integration, requiring constant operator supervision and complex parameter adjustments.
Innovation Solution
A physics-based simulated environment using machine learning to optimize robot control programs, which includes a physically plausible virtual runtime environment, test planning and testing components, and a robot controller that generates virtual test cases, determines control strategies, and adjusts parameters using machine learning algorithms to achieve optimal execution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If simulation frameworks use simplified geometric models and basic collision detection, then real-time performance is improved, but manufacturing precision and measurement precision deteriorate
Solution Approach 1:
The system segments collision detection into multiple levels: simplified bounding volume detection for real-time performance, and detailed geometric model detection for precision verification. This hierarchical approach allows both real-time operation and high precision detection to coexist.
Solution Approach 2:
The system replaces traditional mechanical collision detection methods with sensor-based detection systems that provide both real-time feedback and high precision measurement, eliminating the need to choose between speed and precision.
2Ease of operation
If simulation frameworks require constant operator supervision and manual parameter adjustment, then ease of operation deteriorates, but device complexity increases
Solution Approach 1:
The simulation framework incorporates automated parameter adjustment mechanisms that self-optimize detection parameters and control settings based on real-time performance data, eliminating the need for constant operator supervision while maintaining system precision.
Solution Approach 2:
The system implements closed-loop feedback mechanisms where execution results automatically feed back into parameter optimization, enabling the system to self-correct and improve without manual intervention, thus improving ease of operation without excessive complexity.
3Manufacturing precision
If detailed geometric models are used for collision detection, then manufacturing precision is improved, but productivity deteriorates due to computational load
Solution Approach 1:
The collision detection process is segmented into phases: initial rapid detection using simplified models, followed by detailed verification using precise geometric models only for critical collision scenarios, thus maintaining both precision and productivity.
Solution Approach 2:
The system applies detailed geometric modeling selectively only when needed for critical detection tasks, rather than continuously, thus achieving high precision where required while maintaining overall system productivity through selective application of computational resources.
Data Source
AI summary
A disclosed system includes a physically plausible virtual runtime environment to simulate a real-life environment for a simulated robot and a test planning and testing component to define a robotic task and generate virtual test cases for the robotic task. The test planning and testing component is further operable to generate virtual test cases for the robotic task, determine a control strategy for executing the virtual test cases, and create the physics-based simulated environment. The system further includes a robot controller operable to execute the virtual test cases in parallel in the physics-based simulated environment, measure a success of the execution, and store training and validation data to a historical database to train a machine learning algorithm. The robot controller may continuously execute the virtual test cases and use the machine learning algorithm to adjust parameters of the control strategy until optimal test cases are determined.