Robot Trajectory Correction Models for Fast, Accurate Motion
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Solution Overview
Problem
Existing robot control systems struggle with achieving high accuracy in movements due to delays in servomotor responses, leading to deviations in the actual trajectory from the target trajectory, which complicates convergence and management of robot movements.
Innovation Solution
A control device that includes a data acquirer, robot controller, and a learner to correct geometric deformations in the actual trajectory relative to the ideal trajectory using correction models, allowing for precise movement data adjustments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If the teaching device repeats calculation of teaching points and control of the robot in accordance with the teaching points to reduce trajectory errors, then the trajectory accuracy is improved, but the time required for convergence is elongated due to the increased number of teaching points for complicated target trajectories
Solution Approach 1:
The patent segments the trajectory correction process into multiple phases: initial teaching point calculation, rough correction phase, and fine correction phase. By dividing the correction process and applying different levels of correction intensity at different stages, the system achieves both high accuracy and fast convergence without requiring an excessive number of teaching points for complicated trajectories.
Solution Approach 2:
The patent implements dynamic adjustment of correction strength and teaching point density based on the convergence state. The correction intensity is adjusted adaptively during the learning process, allowing faster convergence in early stages and more precise refinement in later stages, thereby resolving the contradiction between speed and accuracy.
2Manufacturing precision
If the robot controller corrects movement data using the correction model to reduce geometric deformation, then the movement accuracy is improved, but the computational complexity increases
Solution Approach 1:
The patent performs preliminary learning to build correction models before actual robot operations. By pre-calculating correction models based on geometric deformations observed during learning phases, the system stores correction data that can be quickly applied during execution without real-time complex calculations, thus improving accuracy while managing computational complexity.
Solution Approach 2:
The patent creates simplified correction models that capture the essential geometric deformation patterns without requiring full complex trajectory recalculation. These correction models serve as simplified representations that can be efficiently applied to correct movement data, reducing computational burden while maintaining accuracy improvements.
Data Source
AI summary
A control device includes a data acquirer, a robot controller, and a first learner. The first learner calculates a geometric deformation of a first trajectory relative to a first ideal trajectory, based on data on the first trajectory and data on the first ideal trajectory. The first trajectory is an actual trajectory actually defined by a movement of the robot and the first ideal trajectory is an ideal trajectory preferably defined by a movement of the robot when the robot controller controls the robot in accordance with a first learning data set acquired by the data acquirer. The first learner generates a first correction model for correcting a piece of movement data and thus reducing the calculated geometric deformation, based on the calculated geometric deformation and the piece of data contained in the first learning data set.