Cloud Belief World Updates for Dynamic Robot Workcell Control
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Solution Overview
Problem
Real-time robotics control systems face limitations in handling dynamic environments due to their reliance on fixed sensor data, leading to potential system faults and reduced operational efficiency.
Innovation Solution
Implementing a cloud-based belief world system that updates sensor data from various sources, prioritizing trustworthy information and allowing remote access and storage, enabling real-time adjustments and enhanced troubleshooting capabilities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If sensor data is tightly bound to the real-time control cycle, then the system maintains strict timing requirements and avoids fault states, but the system cannot handle dynamic environmental changes and assumes the physical world remains fixed
Solution Approach 1:
The patent segments the belief world into multiple independent footprints, each representing a specific aspect of the workcell state (robot positions, fixture locations, etc.). This segmentation allows individual footprints to be updated independently based on different data sources and timing requirements, enabling the system to maintain real-time control reliability for critical functions while adapting to environmental changes in less time-critical areas.
Solution Approach 2:
The patent introduces a belief world as an intermediary layer between the physical sensors and the real-time control system. This belief world acts as a buffer that can be updated with sensor data asynchronously, allowing the control system to access updated environmental information without being tightly coupled to the real-time control cycle, thus resolving the contradiction between timing reliability and environmental adaptability.
2Loss of information
If multiple sensor sources are integrated, then the system gains comprehensive workcell awareness, but the system complexity increases and data from multiple sources must be reconciled
Solution Approach 1:
The patent applies local quality by assigning different trust levels and update frequencies to different sensor sources based on their reliability and characteristics. Each sensor source can contribute to specific belief world footprints according to its capabilities, allowing the system to integrate multiple sensors without requiring uniform processing of all data, thus reducing integration complexity while maintaining information completeness.
Solution Approach 2:
The patent implements feedback mechanisms where the belief world continuously integrates sensor data and provides updated state information back to the control system. This feedback loop allows multiple sensor sources to be reconciled through a standardized interface, managing complexity by providing a unified view of the workcell state while maintaining comprehensive information from diverse sources.
3Speed
If sensor systems are tightly bound to real-time control cycle, then timing requirements are met, but the physical footprint of on-site infrastructure increases to ensure real-time processing
Solution Approach 1:
The patent extracts the belief world maintenance and sensor data reconciliation functions from the on-site real-time control system and places them in a cloud-based architecture. This extraction allows the on-site system to maintain minimal infrastructure while still achieving real-time response by accessing pre-processed belief world data from the cloud, significantly reducing the physical footprint of on-site infrastructure.
Solution Approach 2:
The patent moves sensor processing and belief world maintenance to a cloud-based dimension, separating these computationally intensive tasks from the edge-based real-time control system. This dimensional shift allows the on-site system to remain lightweight while still achieving real-time performance by leveraging cloud computing resources for data processing and storage.
Data Source
AI summary
Methods, systems, and media comprising; a physical robot in a physical workcell; an onsite execution subsystem that is configured to control the physical robot using a real-time control subsystem; a cloud-based belief world subsystem that is configured to receive and store sensor data captured in the workcell, wherein the onsite execution subsystem is configured to use sensor data stored by the cloud-based belief world subsystem in order to control the robot using the real-time control subsystem.