Tagged Robot Sensor Data Offloading via Context Rules
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Solution Overview
Problem
Robots, especially autonomous or semi-autonomous ones, face challenges in managing large amounts of sensor data due to limited communication bandwidth and reliability in various operational contexts, making it impractical to store all data locally or offload it to remote systems effectively.
Innovation Solution
Implementing a method where sensor data points are tagged based on attributes and context, allowing for dynamic offloading and storage decisions based on transport rules that consider available communication bandwidth, reliability, power, and memory, ensuring that high-priority data is offloaded or stored appropriately while low-priority data is managed locally or discarded as needed.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If all sensor data is stored locally in robot memory, then data availability is improved, but memory capacity is exceeded and system complexity increases
Solution Approach 1:
The patent segments sensor data into different priority levels (e.g., high-priority operational data vs. low-priority diagnostic data) and applies different storage strategies to each segment. Critical data is stored locally with higher redundancy, while less critical data is stored remotely or with lower retention, resolving the contradiction between data availability and memory capacity constraints.
Solution Approach 2:
The patent extracts and removes non-essential sensor data from local robot memory, transferring it to external storage systems or deleting it after brief retention. This extraction of low-value data frees up memory capacity while preserving essential operational data locally, addressing both data availability and memory capacity concerns.
2Quantity of substance
If sensor data is offloaded to remote computing system, then local memory usage is reduced, but communication bandwidth and reliability requirements increase system complexity
Solution Approach 1:
The patent applies local quality by retaining different types of sensor data at different locations based on their specific requirements. Time-critical control data remains locally stored for immediate access, while historical or analytical data is offloaded to remote systems. This differentiated approach reduces local memory usage without requiring continuous high-bandwidth communication for all data types.
3Productivity
If high-priority sensor data is selectively offloaded, then data management efficiency is improved, but tagging and processing overhead increases
Solution Approach 1:
The patent implements preliminary action by pre-tagging sensor data with priority metadata at the point of generation, before any storage or transmission decisions are made. This upfront classification enables efficient subsequent processing where tagged data can be automatically routed to appropriate storage locations based on priority levels, improving data management efficiency while keeping processing overhead manageable through automated rule-based decisions.
Data Source
AI summary
Methods, apparatus, systems, and computer-readable media are provided for creating, storing, and/or offloading tagged robot sensor data. In various implementations, a first plurality of sensor data points that are sampled by one or more sensors associated with a robot and that share a first attribute may be identified. Each of the first plurality of sensor data points may be tagged with a first tag, which may be indicative of the first attribute. A context in which a robot is operating may be identified. A first transport rule that governs how sensor data points tagged with the first tag are treated when the robot operates in the context may then be identified. At least a subset of the first plurality of tagged sensor data points may then be offloaded from the robot and/or stored locally on the robot pursuant to the first transport rule.