QoE Analysis for Delay-Critical Cloud Robotics Links
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Solution Overview
Problem
Cloud robotics systems face challenges in managing deadline critical robotic applications, particularly when alternative trajectories are calculated in the cloud, due to delays and jitter caused by mobile communication networks and cloud computing environments.
Innovation Solution
A method for analyzing Quality of Experience (QoE) in delay critical robotic applications, which involves determining whether QoE degradation is present, identifying the root cause based on performance indicators, and performing corrective actions such as resource reallocation or adjusting 5QI mappings to ensure sufficient QoE.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Power
If cloud computing environments are used to provide computational power for trajectory calculations, then computational capacity is improved, but delay and jitter increase
Solution Approach 1:
The patent segments the cloud computing system into multiple edge computing nodes distributed closer to robots. This segmentation allows trajectory calculations to be performed at edge nodes with lower latency, while still providing cloud-level computational power through the distributed network of nodes.
Solution Approach 2:
The patent introduces edge computing nodes as intermediaries between robots and the central cloud. These intermediary nodes process trajectory calculations locally, reducing the delay and jitter associated with direct cloud communication while maintaining access to cloud-based computational resources when needed.
2Reliability
If digital twins are used to simulate and test control decisions, then safety is improved, but computational resource requirements increase
Solution Approach 1:
The patent divides digital twin simulations into multiple parallel instances running on distributed edge computing nodes. Each node handles specific simulation scenarios, enabling comprehensive safety testing without concentrating all computational demands on a single system.
Solution Approach 2:
The patent implements selective digital twin simulation, running full simulations only when necessary for critical safety assessments and using simplified models for routine trajectory validation. This partial action approach maintains safety while reducing overall computational resource consumption.
3Adaptability or versatility
If mobile communication networks are used for robot-cloud connectivity, then flexibility is improved, but transmission quality deteriorates
Solution Approach 1:
The patent uses edge computing nodes as intermediaries that maintain persistent local caches of trajectory data and robot states. These intermediaries reduce the frequency and criticality of mobile network transmissions, improving transmission quality while maintaining the flexibility of mobile connectivity.
Solution Approach 2:
The patent pre-loads trajectory calculations and safety data at edge nodes before they are needed by robots. This preliminary action reduces the amount of critical data that must be transmitted over mobile networks in real-time, improving transmission reliability while maintaining system flexibility.
Data Source
AI summary
A technique for analyzing Quality of Experience, QoE, of a delay critical robotic application in a cloud robotics system is disclosed, the robotic application involving use of a cloud based service by a robot, the service being provided to the robot from a cloud using a connection over a mobile communication network. A method implementation of the technique comprises triggering (S202) determining whether a QoE measure associated with the robotic application exceeds a threshold to assess whether QoE degradation is present, and, when it is determined that QoE degradation is present, triggering (S204) identifying a root cause for the QoE degradation based on one or more performance indicators observed within the mobile communication network and associated with the connection.