Robot Assembly Pose Interpolation for Low-Latency Motion Control
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Solution Overview
Problem
Existing robot control systems face issues with delayed detection and interpolation of object positions, leading to noise, unsteadiness, and latency due to the mismatch between detection intervals and control cycles.
Innovation Solution
A method that modifies pose data to reduce interpolation latency by adjusting the time intervals for robot control, allowing for faster sampling rates and shorter control cycles, and combining data from multiple sensors to improve precision and stability, particularly using quaternions and piecewise polynomials for orientation and velocity calculations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If pose data are detected at lower sampling rates to reduce measurement noise, then measurement precision is improved, but control responsiveness and productivity deteriorate due to larger time intervals
Solution Approach 1:
The system performs preliminary interpolation of pose data in advance to generate intermediate values at higher sampling rates. By pre-calculating modified pose data using polynomial interpolation between detected pose values, the system prepares control data ahead of time, eliminating the need for real-time interpolation during control cycles and thus improving responsiveness without sacrificing measurement precision.
Solution Approach 2:
The system dynamically adjusts the sampling rate by generating modified pose data at variable intervals. Instead of using fixed low-rate detection data directly, the system creates high-rate modified pose data through interpolation, allowing the control system to operate at optimal sampling rates that balance measurement accuracy with real-time control requirements.
2Manufacturing precision
If fine interpolation is performed in axis space to match control cycles, then control precision is improved, but system complexity and computational load increase
Solution Approach 1:
The system performs interpolation in pose space (position and orientation) rather than in robot axis space. By interpolating Cartesian pose data and then transforming to joint space, the system avoids complex trigonometric calculations and singularities associated with direct axis-space interpolation, thereby reducing computational complexity while maintaining control precision.
Solution Approach 2:
The system replaces complex real-time axis-space interpolation calculations with pre-computed pose-space interpolation. This substitution of computational approach reduces the real-time processing burden on the control system, replacing intensive mechanical-style calculations with more efficient mathematical operations in pose space.
3Use of energy by moving object
If detection time intervals are increased to reduce data processing load, then energy consumption is reduced, but latency and loss of time increase
Solution Approach 1:
The system creates modified pose data as a computational copy of the original detected pose data. Instead of processing raw sensor data at high rates, the system generates interpolated copy data that represents the object's pose at intermediate time points. This copying approach allows low-rate detection to effectively provide high-rate control information, reducing energy consumption while minimizing latency.
Data Source
AI summary
A method for the control of a robot assembly having at least one robot. The method includes acquiring pose data from an object arrangement having at least one object, which data has a first time interval; determining modified pose data from the object arrangement, which data has a second time interval that is larger or smaller than the first time interval, or is equal to the first time interval, on the basis of the acquired pose data; and controlling the robot assembly on the basis of said modified pose data.