Robot Joint Gear Stiffness Estimation for Wear-Adaptive Control
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Solution Overview
Problem
Existing robot joint controllers struggle to accurately account for time-varying joint stiffness due to wear, leading to reduced precision and increased risk of unplanned downtime.
Innovation Solution
A method to estimate the gear stiffness of robot joint gears online using commonly available sensors, allowing for dynamic model updates and predictive maintenance without the need for expensive force/torque sensors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If off-line identification procedures are used to estimate joint stiffness, then the stiffness information can be obtained, but the robot cannot conduct other tasks during the procedure and the stiffness data becomes outdated due to wear over time
Solution Approach 1:
The patent transitions from static off-line stiffness identification to dynamic on-line estimation. The system continuously updates joint stiffness parameters during robot operation by processing sensor data from ongoing tasks, eliminating the need for separate calibration periods and adapting to wear-induced stiffness changes in real-time.
Solution Approach 2:
The patent implements a feedback mechanism where sensor measurements from regular operation are continuously processed to update joint stiffness estimates. The system uses measured joint positions, velocities, accelerations, and motor currents to compute stiffness parameters, which are then fed back to improve control accuracy without interrupting robot tasks.
2Measurement precision
If expensive force/torque sensors are installed to measure joint stiffness, then accurate real-time stiffness data can be obtained, but the system cost increases significantly
Solution Approach 1:
The patent uses motor current and position sensor data as intermediary measurements to indirectly estimate joint stiffness. Instead of directly measuring stiffness with expensive force/torque sensors, the system processes readily available motor current signals combined with kinematic data to compute stiffness parameters, achieving accurate estimation without additional expensive hardware.
Solution Approach 2:
The patent replaces mechanical force/torque sensing systems with an electrical measurement approach. By analyzing motor current signals and combining them with kinematic information from position sensors, the system substitutes expensive mechanical force sensors with cheaper electrical measurements and computational processing.
3Device complexity
If the dynamic model does not account for gear flexibility, then the control system is simpler, but the robot movement accuracy and disturbance identification capability are reduced
Solution Approach 1:
The patent dynamically changes the stiffness parameter values in the dynamic model based on real-time estimation from sensor data. Instead of using fixed or simplified stiffness assumptions, the system continuously updates the stiffness parameters to reflect actual wear conditions, maintaining high movement accuracy while adapting to changing gear conditions over time.
Data Source
AI summary
A method of obtaining the gear stiffness of a robot joint gear of a robot joint of a robot arm, where the robot joint is connectable to at least another robot joint. The robot joint comprises a joint motor having a motor axle configured to rotate an output axle via the robot joint gear. The method comprises the steps of: —applying a motor torque to the motor axle using the joint motor; —obtaining the angular position of the motor axle; —obtaining the angular position of the output axle; —determining the gear stiffness based on at least the angular position of the motor axle, the angular position of the output axle and a dynamic model of the robot arm.