Robot-Cloud Diagnostics With Adaptive Data Collection and Bandwidth Control
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Solution Overview
Problem
Conventional autonomous machine systems face challenges in managing large data volumes, limited bandwidth, and resource constraints, particularly in mobile robots, which affect efficient data collection and transmission and local autonomy operations.
Innovation Solution
A system and method for adaptive diagnostics and data collection in a connected robot-cloud environment that optimizes robot resources by selectively monitoring sensor streams, processing data with varying sampling intervals, compressing data, aggregating before transmission, and managing bandwidth through a software agent that dynamically adjusts resource usage based on current utilization and operational status.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If traditional approaches for connecting devices to the Internet are used for robots, then data collection and transmission can be established, but the amount of data managed on the robot becomes too large, consuming excessive computational resources and bandwidth
Solution Approach 1:
The patent extracts and separates critical data from the bulk data stream. The software agent identifies and prioritizes only the most essential diagnostic and operational data for transmission to the cloud, while filtering out redundant or less critical information. This extraction approach reduces the data volume requiring complex processing and transmission resources while maintaining the integrity of essential information.
Solution Approach 2:
The patent segments data into different priority levels and categories. The software agent divides the data stream into critical operational data, diagnostic data, and non-critical data, applying different processing and transmission strategies to each segment. This segmentation allows the system to manage data more efficiently by allocating computational resources only to high-priority segments.
2Reliability
If continuous data transmission is implemented for monitoring robot status, then comprehensive monitoring is achieved, but bandwidth consumption increases and latency increases
Solution Approach 1:
The patent implements periodic action by transmitting data at variable intervals rather than continuously. The software agent dynamically adjusts the sampling and transmission intervals based on the robot's operational state, network conditions, and data criticality. Critical events trigger immediate transmission, while normal operations use extended intervals, reducing overall bandwidth consumption while maintaining monitoring reliability.
Solution Approach 2:
The patent employs feedback mechanisms where the software agent continuously monitors data characteristics, network conditions, and robot state, then adjusts transmission parameters accordingly. This feedback loop enables the system to optimize bandwidth usage by increasing transmission frequency only when necessary (e.g., during critical events or anomalies) and reducing frequency during stable operations.
3Productivity
If computational resources are allocated for extensive data processing on the robot, then data analysis capability is improved, but resources available for local autonomy operations decrease
Solution Approach 1:
The patent introduces a software agent as an intermediary that performs lightweight preprocessing and filtering of data before it requires extensive processing. This agent acts as a gatekeeper, preparing data in a format that reduces the computational burden on the robot's main processing resources, thereby preserving those resources for local autonomy operations while still enabling effective data analysis.
Solution Approach 2:
The patent applies preliminary action by performing initial data filtering, aggregation, and prioritization before main processing occurs. The software agent prepares data in advance by removing redundancies, categorizing information, and pre-processing critical data, which significantly reduces the computational workload required for subsequent analysis while maintaining data analysis capability.
Data Source
AI summary
A system and method for adaptive diagnostics and data collection in a connected robot-cloud environment allows for the management and use of date from a robot or fleet of robots to ensure the efficient utilization thereof. The data is collected from the robots via a software agent and is transmitted to an interface that allows action from an end-user.