Rangefinder-Robot Calibration for Accurate Pose Alignment
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Solution Overview
Problem
Robotic systems with rangefinders and manipulators face errors due to the combination of these components, leading to inaccuracies in determining pose information, which is crucial for tasks like grasping objects with sub-centimeter accuracy in unstructured environments.
Innovation Solution
A system and method that involve a processor-based device with a rangefinder and manipulator, where the processor obtains and optimizes pose information by updating a model with parameters based on calibration data, allowing for accurate transformation and correction of pose information for improved robotic operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If rangefinder and manipulator are combined in a robotic system, then the system can perform automated tasks with both sensing and actuation capabilities, but errors accumulate in pose information determination reducing measurement precision
Solution Approach 1:
The system performs preliminary calibration by collecting pose information from both the rangefinder and manipulator across multiple poses before actual operation. This pre-characterization of the mismatch allows the system to compensate for errors during subsequent automated tasks, maintaining precision while preserving automation capability.
Solution Approach 2:
The system continuously compares rangefinder pose measurements with manipulator pose information and uses the observed mismatch to correct rangefinder measurements. This feedback loop ensures that pose information accuracy is maintained despite the combination of multiple components, allowing the system to achieve sub-centimeter accuracy in unstructured environments.
2Measurement precision
If calibration data is collected across multiple poses to optimize the mismatch model, then measurement precision improves, but the time required for calibration increases
Solution Approach 1:
The system collects pose information from a finite set of poses that is sufficient to characterize the mismatch model without requiring exhaustive coverage of all possible configurations. This partial action approach achieves adequate calibration precision while limiting calibration time to a practical duration.
Solution Approach 2:
The system optimizes parameters of the mismatch model by collecting data across multiple poses and using optimization algorithms to find the best-fit parameters. This parameter optimization process efficiently captures the essential characteristics of the rangefinder-manipulator mismatch without requiring excessive calibration poses, balancing precision improvement with time constraints.
Data Source
AI summary
Systems, devices, articles, and methods, described in greater detail herein, including robotic systems which include at least one rangefinder, at least one manipulator, and at least one processor in communication with the at least one rangefinder, and methods of operation of the same. The at least one processor obtains rangefinder pose information which represents, at least, the at least one manipulator in a plurality of poses. The at least one processor obtains manipulator pose information, optimizes a model of mismatch between the rangefinder pose information and the manipulator pose information, wherein the model of mismatch includes a plurality of parameters, and updates at least one processor readable storage device with the plurality of parameters based at least in part on the optimization.