Cloud Robotics QoS Mapping for QoC-Aware Robot Control
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Solution Overview
Problem
Current robot control methods do not optimize resource utilization in cloud robotics systems, particularly in terms of accuracy and production speed, and fail to consider frequency spectrum scarcity and network resource limitations in wireless communication.
Innovation Solution
A method for mapping control messages in a cloud robotics system to different Quality of Service (QoS) classes based on Quality of Control (QoC) tolerances, allowing high-priority messages to use stringent QoS classes and lower-priority messages to use best effort delivery, with dynamic adjustments based on QoC feedback to maintain target quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If stringent QoS classes are applied to all control messages to ensure high accuracy, then manufacturing precision is improved, but productivity decreases due to slower robot movement and increased network resource consumption
Solution Approach 1:
The patent applies different QoS class mappings to different control messages based on their specific QoC tolerance requirements. Critical control messages with low QoC tolerance are mapped to stringent QoS classes (e.g., URLLC) to ensure high accuracy, while non-critical messages with high QoC tolerance are mapped to best-effort QoS classes to maximize speed. This localized differentiation resolves the contradiction by providing high precision only where necessary.
Solution Approach 2:
The patent segments the control message stream into different categories based on QoC tolerance levels. By dividing control messages into critical and non-critical groups with distinct QoS mappings, the system achieves both high accuracy for critical operations and high productivity for non-critical operations, eliminating the need to apply uniform stringent QoS to all messages.
2Reliability
If stringent QoS classes are used for all control messages, then reliability of control is improved, but network resource consumption increases
Solution Approach 1:
The patent applies stringent QoS classes only to control messages that have low QoC tolerance and require high reliability, while applying best-effort QoS to messages with high QoC tolerance. This localized application of high-reliability QoS ensures dependable delivery for critical messages while conserving network resources for non-critical messages.
Solution Approach 2:
Instead of applying full stringent QoS to all control messages, the patent applies partial QoS optimization by selectively applying high-reliability QoS only to the portion of messages that actually require it (those with low QoC tolerance), avoiding excessive network resource consumption while maintaining sufficient reliability.
3Productivity
If QoS classes are dynamically adjusted based on network conditions, then productivity is improved by allowing faster robot movement, but device complexity increases due to dynamic mapping adjustments
Solution Approach 1:
The patent implements dynamic QoS class mapping that adapts to changing network conditions and robot task requirements. The mapping between control messages and QoS classes is not fixed but can be adjusted in real-time based on network status and QoC tolerance levels, enabling the system to optimize productivity while maintaining quality.
Solution Approach 2:
The patent incorporates feedback mechanisms where the system monitors network conditions and QoC tolerance levels, then adjusts QoS mappings accordingly. This feedback-driven dynamic adjustment allows the system to automatically optimize robot movement speed and network resource usage without manual intervention, managing complexity through automated adaptation.
4Productivity
If QoS mapping is adjusted based on QoC tolerance levels, then resource utilization is optimized, but measurement precision requirements increase for QoC tolerance assessment
Solution Approach 1:
The patent changes the parameter being measured from absolute position accuracy to QoC tolerance levels, which represent the maximum acceptable accuracy degradation. By measuring and categorizing control messages based on their QoC tolerance parameters, the system can optimize network resource utilization while maintaining sufficient measurement precision for decision-making.
Data Source
AI summary
A technique for performing Quality of Service, QoS, control regarding control messages communicated from a robot controller to a robot via a mobile communication network in a cloud robotics system is disclosed. A method implementation of the technique comprises triggering applying a mapping of control messages communicated from the robot controller to the robot to QoS classes among a plurality of data session related QoS classes supported by the mobile communication network for transmission of traffic via the mobile communication network, wherein each of the control messages is mapped to a respective QoS class depending on a Quality of Control, QoC, tolerance associated with the respective control message.