Autonomous Robot Map Calibration Using RF Tag Localization
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Solution Overview
Problem
Autonomous robots in manufacturing environments face challenges in accurately calibrating their local maps and trajectories, which can lead to navigation errors and require manual intervention for initial localization.
Innovation Solution
A method and system for calibrating an autonomous robot's map and trajectory by obtaining localization data, determining a confidence score, and using RF signals from tags to update the local position coordinate, thereby automating the calibration process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual calibration routines are used to ensure accurate navigation, then navigation accuracy is improved, but operator intervention time and complexity increase
Solution Approach 1:
The autonomous robot performs self-calibration by automatically determining its initial localization estimate using localization sensors and comparing detected objects with reference objects from the global map, eliminating the need for operator intervention while maintaining navigation accuracy
Solution Approach 2:
The system uses feedback from localization sensors to detect objects in the environment, compares them with reference objects from the global map, and automatically adjusts the initial localization estimate based on the comparison results, enabling accurate self-calibration
2Measurement precision
If operator input is required for initial localization, then calibration accuracy is improved, but automation level decreases
Solution Approach 1:
The autonomous robot independently performs the entire calibration process by using its localization sensors to detect objects, comparing them with the global map, and automatically determining its initial localization estimate without any operator input
Solution Approach 2:
The system replaces manual operator input with automated sensor-based detection and computational comparison, substituting human mechanical calibration actions with electronic sensing and algorithmic processing
3Adaptability or versatility
If local maps are uniquely defined for each robot type, then navigation specificity is improved, but map conversion complexity increases
Solution Approach 1:
The system uses a universal global map that can be shared across different robot types, with the map containing reference objects that any robot can detect and compare, eliminating the need for multiple unique local maps while maintaining robot-specific navigation capabilities
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The solution enables autonomous robots to accurately update their local maps and trajectories without operator input, improving navigation accuracy and reducing manual calibration efforts.
Implementation Method 1
obtaining localization data from a localization sensor of the autonomous robot and determining whether a calibration condition of the autonomous robot is satisfied based on the localization data
Implementation Method 2
determining a master position coordinate of the autonomous robot based on a plurality of radio frequency (RF) signals broadcasted by a plurality of RF tags
Data Source
AI summary
A method for calibrating a map of an autonomous robot, a trajectory of the autonomous robot, or a combination thereof includes obtaining localization data from a localization sensor of the autonomous robot and determining whether a calibration condition of the autonomous robot is satisfied based on the localization data. The method includes, in response to the calibration condition being satisfied: determining a master position coordinate of the autonomous robot based on a plurality of radio frequency (RF) signals broadcasted by a plurality of RF tags, converting the master position coordinate to a local position coordinate of the autonomous robot, and selectively updating the map, the trajectory, or a combination thereof based on the local position coordinate of the autonomous robot.