Robot Arm Parameter Estimation for Precise Joint Calibration
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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, especially in environments like warehouses where precise object handling is required.
Innovation Solution
A computing system that communicates with a robot arm to perform calibration by selecting specific joints or segments, generating movement commands, and using sensor data to estimate physical properties such as friction and center of mass, allowing for more accurate control and trajectory planning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If robot calibration is performed using traditional methods, then the robot can execute basic tasks, but the accuracy and precision of complex tasks remain insufficient
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, collecting sensor data from specific components, and estimating physical properties for individual segments. This segmentation allows the system to focus computational resources on critical calibration tasks rather than calibrating the entire robot system simultaneously, thereby improving measurement precision without proportionally increasing overall system complexity.
Solution Approach 2:
The system performs preliminary actions by pre-selecting joints or arm segments that require calibration before executing the full calibration sequence. Movement commands are generated in advance based on predefined calibration trajectories, and sensor data collection is prepared beforehand. This preliminary preparation ensures that when calibration is executed, the robot is already positioned and configured optimally, improving measurement accuracy while reducing the complexity of real-time calibration coordination.
2Productivity
If the robot arm moves faster to improve productivity, then task completion time decreases, but measurement noise increases reducing control precision
Solution Approach 1:
The system dynamically changes operational parameters based on the calibration stage and task requirements. During calibration phases, the robot arm operates at controlled speeds that optimize measurement accuracy for estimating physical properties. During actual task execution, the system adjusts speed parameters to achieve desired productivity levels while applying the calibrated parameters to maintain control precision. This parameter adaptation allows the system to optimize for either speed or precision depending on the operational context.
3Measurement precision
If comprehensive sensor data collection is performed to improve calibration accuracy, then measurement precision increases, but data processing time and computational load increase
Solution Approach 1:
The system extracts and focuses on collecting sensor data from specifically selected joints or arm segments that are most critical for the calibration task at hand, rather than collecting comprehensive data from all robot components simultaneously. By extracting only the necessary data subsets, the system achieves sufficient calibration accuracy for the selected components while significantly reducing the overall data processing time and computational load. This selective data extraction approach maintains measurement precision for critical parameters without the overhead of processing entire robot system data.
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.