Robot Arm Calibration for Friction and Center-of-Mass Estimation
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Solution Overview
Problem
Robots lack the sophistication to accurately and precisely execute complex tasks due to inadequate calibration, which is essential for mimicking human-like interactions and movements in environments like warehouses and retail settings.
Innovation Solution
A computing system that performs robot calibration by selecting specific joints or arm segments, generating movement commands, and receiving actuation and movement data to estimate friction and center of mass parameters, allowing for more accurate control and trajectory planning of robot arms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If robot calibration is performed to improve movement accuracy and precision, then the robot's ability to execute complex tasks improves, but the device complexity and calibration process time increase
Solution Approach 1:
The calibration process is segmented into distinct phases: selecting specific joints or arm segments for calibration, generating targeted movement commands for selected components, receiving actuation and movement data separately, and estimating parameters (friction, center of mass) individually. This segmentation allows systematic calibration of complex robot arms without overwhelming complexity.
Solution Approach 2:
The system performs preliminary actions by pre-selecting which joints or arm segments require calibration before executing the full calibration sequence. Movement commands are generated in advance with known trajectories, and the system prepares to receive actuation data for the selected components, streamlining the overall calibration process.
2Measurement precision
If comprehensive calibration data collection is performed to improve parameter estimation accuracy, then the precision of friction and center of mass estimates improves, but the time required for calibration and loss of time increases
Solution Approach 1:
The system applies partial action by selecting only specific joints or arm segments for calibration rather than calibrating the entire robot arm. This allows adequate parameter estimation for critical components while reducing overall calibration time. The movement commands are designed to provide sufficient data for accurate parameter estimation without requiring exhaustive measurement of all possible parameters.
Solution Approach 2:
The calibration system uses the robot's own actuation data and movement data from its operational components to estimate parameters. The robot arm itself provides the test data through its own motors and sensors during controlled movements, eliminating the need for external calibration equipment and reducing calibration time while maintaining accuracy.
3Manufacturing precision
If detailed sensor data collection is implemented to improve friction and center of mass parameter estimates, then the control precision improves, but the quantity of data processing and device complexity increase
Solution Approach 1:
The system extracts only the essential data needed for parameter estimation: actuation data (torque/force) and movement data (position, velocity, acceleration) from selected joints or arm segments. By extracting only these critical parameters rather than processing all available sensor data, the system achieves accurate friction and center of mass estimates without overwhelming data processing complexity.
Solution Approach 2:
The calibration process changes parameters by estimating friction coefficients and center of mass locations for selected components based on collected data. These parameter changes are then applied to update the robot's control model, improving control precision for subsequent operations without requiring permanent structural modifications or complex ongoing data processing.
Data Source
AI summary
A computing system and method for estimating friction and/or center of mass (CoM) are presented. The system may perform the method by selecting at least one of: (i) a first joint from among a plurality of joints, or (ii) a first arm segment from among a plurality of arm segments. The computing system further outputs a set of one or more movement commands for causing robot arm movement that includes relative movement between the first arm segment and a second arm segment via the first joint, and receiving a set of actuation data and a set of movement data associated with the first joint or the first arm segment. The computing system further determines, based on the set of actuation data and the set of movement data, at least one of: (i) a friction parameter estimate or (ii) a CoM estimate.