Dynamic Coordinate System for Multi-Sensor Robot Control
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Solution Overview
Problem
In dynamic environments, existing methods fail to efficiently translate and share 2D or 3D coordinates between multiple sensors and robots, especially when their relative positions change unpredictably, making it difficult to control robotic devices accurately.
Innovation Solution
A method is developed to create a common coordinate system by collecting and analyzing sequences of images from multiple sensors and cameras, including depth maps, to determine relative positions and create dynamic transformation matrices, allowing for real-time coordinate conversion and control of robotic actions through gaze-based interfaces.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If static coordinate translation methods are used in dynamic environments, then initial setup effort is reduced, but coordinate accuracy deteriorates as robots and sensors change positions
Solution Approach 1:
The system transitions from static coordinate translation to dynamic coordinate translation by continuously tracking the positions of robots and sensors. The coordinate translation function is updated in real-time based on current positions, allowing the system to adapt to changing environments while maintaining coordinate accuracy without requiring frequent manual re-setup.
Solution Approach 2:
The system implements feedback mechanisms where the positions of robots and sensors are continuously monitored and fed back to the coordinate translation function. This feedback loop enables automatic adjustment of coordinate transformations, ensuring accuracy is maintained even as components move, thereby resolving the contradiction between ease of setup and measurement precision.
2Measurement precision
If dynamic coordinate translation is implemented, then coordinate accuracy is maintained in changing environments, but system complexity increases
Solution Approach 1:
The system employs a universal coordinate translation function that can handle multiple sensors and robots of different types. This multi-functional approach consolidates what would otherwise require multiple specialized translation systems, maintaining coordinate accuracy across diverse components while reducing overall system complexity through standardization.
Solution Approach 2:
The patent introduces a central coordinate translation module that acts as an intermediary between various sensors and robots. This mediator handles all coordinate transformations, simplifying the architecture by centralizing the complex translation logic rather than requiring direct point-to-point translation mechanisms between each component pair.
3Adaptability or versatility
If multiple sensors and robots are integrated, then system versatility is improved, but coordinate translation complexity increases
Solution Approach 1:
The system implements a universal coordinate translation framework that can accommodate multiple sensors and robots with different coordinate systems. This multi-functional translation engine handles diverse component types through a unified approach, maintaining system versatility while managing translation complexity through standardization and abstraction.
Data Source
AI summary
An adaptive learning interface system for end-users for controlling one or more machines or robots to perform a given task, combining identification of gaze patterns, EEG channel's signal patterns, voice commands and/or touch commands. The output streams of these sensors are analyzed by the processing unit in order to detect one or more patterns that are translated into one or more commands to the robot, to the processing unit or to other devices. A pattern learning mechanism is implemented by keeping immediate history of outputs collected from those sensors, analyzing their individual behavior and analyzing time correlation between patterns recognized from each of the sensors. Prediction of patterns or combination of patterns is enabled by analyzing partial history of sensors' outputs. A method for defining a common coordinate system between robots and sensors in a given environment, and therefore dynamically calibrating these sensors and devices, is used to share characteristics and positions of each object detected on the scene.