Cloud Robot Control With Predictive Communication Degradation
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Solution Overview
Problem
Cloud-based robot control methods require a stable communication environment to prevent accidents caused by malfunction, but existing methods fail to effectively manage communication performance fluctuations, leading to potential safety issues when communication quality deteriorates.
Innovation Solution
Implementing a predictive model using machine learning to anticipate communication performance degradation, allowing the robot to autonomously adjust its operations, such as reducing velocity or altering data transmission priorities, to maintain safety and functionality even during poor communication conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If cloud-based robot control is implemented to reduce processor size and manufacturing costs, then device complexity and manufacturing cost are reduced, but communication stability and safety deteriorate when communication performance degrades
Solution Approach 1:
The system performs preliminary actions by predicting future communication performance degradation using machine learning models before actual degradation occurs. When degradation is predicted, the robot proactively adjusts its operations (such as reducing velocity or pausing) in advance, preventing accidents that would occur during communication failures. This shifts the timing of safety measures from reactive to proactive.
Solution Approach 2:
The system implements feedback by continuously monitoring current communication performance metrics (packet loss rate, latency, bandwidth) and using this feedback to update predictions of future communication performance. The predicted future performance then feeds back into operation adjustment decisions, creating a closed-loop control system that adapts to changing communication conditions.
2Productivity
If robot operations continue at normal speed regardless of communication conditions, then productivity is maintained, but safety deteriorates when communication performance degrades
Solution Approach 1:
The system applies dynamics by making robot operation parameters (velocity, acceleration, operation range) dynamic rather than fixed. The operation speed is continuously adjusted based on predicted future communication performance. When communication degradation is predicted, the robot dynamically reduces speed or pauses operations; when communication is stable, the robot operates at full speed, optimizing both safety and productivity adaptively.
3Reliability
If communication performance monitoring is enhanced to ensure safety, then reliability is improved, but device complexity and processing requirements increase
Solution Approach 1:
The system introduces an intermediary machine learning prediction model that acts as a mediator between raw communication performance data and robot control decisions. Instead of directly processing complex communication metrics and making control decisions, the prediction model transforms communication performance data into predicted future performance outcomes, simplifying the control logic while enhancing safety through proactive predictions.
Data Source
AI summary
The embodiments relate to a robot and a server communicating with the robot, the robot being driven by using at least one among a driving wheel, a propeller, and a manipulator moving at least one joint.