Robot Parameter Tuning to Bridge Simulation-Reality Gaps
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Solution Overview
Problem
Industrial robots face limitations in execution speed due to physical and dynamic properties, leading to overshoots and residual vibrations, and control algorithms developed for simulated systems often fail to perform in real-world environments due to the reality gap between simulation and reality.
Innovation Solution
A simulation-in-the-loop method and system for simultaneous update of system model and control parameters on both real and simulated robots, bridging the reality gap by using a processor-based parameter tuning system that includes a robot simulator and controller to optimize control parameters and physical parameters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If the robot operates at higher speed to improve productivity, then execution cycle duration is reduced, but overshoots and residual vibrations occur due to physical and dynamic limitations
Solution Approach 1:
The system performs preliminary identification of robot dynamic parameters (mass, inertia, friction) before control execution. This pre-characterization of the robot's physical properties enables the controller to pre-compensate for inertial effects and friction forces, allowing high-speed operation without overshoot or vibration by anticipating and counteracting dynamic disturbances before they occur
Solution Approach 2:
The system implements feedback control using identified dynamic parameters to continuously adjust control commands. The controller uses the characterized friction and inertia properties to generate compensatory control signals that counteract predicted motion errors, maintaining precision during high-speed operation while enabling shorter execution cycles
2Ease of manufacture
If control algorithms are developed in simulation for training and testing, then cost and safety concerns are alleviated, but the algorithms fail to perform in real-world environments due to the reality gap
Solution Approach 1:
The system identifies and characterizes the actual physical parameters (mass, inertia, friction coefficients) of the real robot through experimental data collection and processing. By updating the simulation model with these empirically determined parameters, the simulation accurately reflects real-world dynamics, allowing algorithms trained in simulation to transfer successfully to the physical robot and eliminating the reality gap
Solution Approach 2:
The system creates an accurate computational copy of the real robot's dynamic properties by identifying and storing its physical parameters. This digital twin or simulated model replicates the real robot's mass, inertia, and friction characteristics, enabling realistic simulation-based training that produces control algorithms effective on the actual hardware
Data Source
AI summary
A system for parameter tuning for robotic manipulators is provided. The system includes an interface configured to receive a task specification, a plurality of physical parameters, and a plurality of control parameters, wherein the interface is configured to communicate with a real-world robot via a robot controller. The system further includes a memory to store computer-executable programs including a robot simulation module, a robot controller, and an auto-tuning module a processor, in connection with the memory. In this case, the processor is configured to acquire, in communication with the real-world robot, state values of the real-world robot, state values of the robot simulation module, simultaneously update, by use of a predetermined optimization algorithm with the auto-tuning module, an estimate of one or more of the physical, and said control parameters, and store the updated parameters.