Robot-Camera Calibration Error Detection Using 3D Contact Feedback
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Solution Overview
Problem
Robotic manipulators experience operation-induced errors in calibration due to positional changes of the camera and robotic arm, leading to inaccurate task execution, which can result in accidents, especially in precise environments like the medical field, and existing error calculation methods do not fully address the issue of deviation in movement between the robotic arm and camera.
Innovation Solution
A system and method that utilize a 3D test object with embedded sensors and a CAD model to detect, map, and estimate arm poses for a robotic manipulator, allowing the end effector to contact the test object and determine errors in translational and rotational calibration, specifically in x-axis, y-axis, z-axis, roll, pitch, or yaw, using processor-executable instructions and feedback from sensors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If robotic manipulator operates over time with camera coupling, then task automation capability is improved, but calibration error accumulates due to operation-induced positional changes
Solution Approach 1:
The system performs preliminary error determination by comparing estimated arm poses with actual arm poses before executing tasks. This advance calibration check prevents accumulation of positioning errors during operation, maintaining reliability while preserving automation capability.
2Adaptability or versatility
If manual intervention adjusts camera or robotic arm position, then operational flexibility is improved, but mapping accuracy deteriorates due to disturbed calibration
Solution Approach 1:
The system implements feedback by continuously monitoring the positional relationship between camera and robotic arm. When manual intervention disturbs the calibration, the error determination module detects the deviation and provides correction information, restoring mapping accuracy while preserving operational flexibility.
3Device complexity
If limited error calculation is used, then computational complexity is reduced, but task execution accuracy is insufficient for precise operations
Solution Approach 1:
The error determination process is segmented into distinct modules: test object detection, contact point identification, arm pose estimation, and error calculation. This segmentation enables comprehensive six-degree-of-freedom error analysis without excessive computational complexity, achieving both precision and efficiency.
Data Source
AI summary
Disclosed herein is a device, system and method for determining error in robotic manipulator-to-camera calibration. The method includes detecting a test object by a camera coupled to a robotic manipulator. One or more test points are identified on the test object based on a CAD model and pre-defined contact points corresponding to the test object. Arm poses are determined for the robotic manipulator to reach the test points on the 3D test object by using current robotic manipulator-to-camera calibration. While driving an end effector of the robotic manipulator based on the arm poses, any contact of the end effector on the 3D test object is recorded upon receiving a feedback from the 3D test object. An error is determined in the current robotic manipulator-to-camera calibration based on current position of the end effector relative to the one or more test points on the 3D test object.