Robotic Sensor Checkpoint Tracing for Execution Latency Analysis
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Solution Overview
Problem
In complex robotic systems like autonomous vehicles, retracing events to identify differences between expected and actual behavior is challenging due to high data volumes and potential skipping of processing steps, leading to uncertainty in decision-making.
Innovation Solution
An automated device monitoring system that generates data logs with timestamps for sensor data, performs execution flows, fuses results, and uses these to determine latency and take corrective actions, while optimizing data storage by passing logs without retaining copies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the system processes and logs all sensor data with detailed timestamps for every checkpoint, then the ability to trace execution flows and identify latency sources is improved, but the data storage requirements and system complexity increase
Solution Approach 1:
The patent segments the execution flow into discrete checkpoints, each with its own timestamp and identifier. This segmentation allows precise tracking of data progression through the system without requiring complex centralized logging, as each checkpoint independently records its state.
Solution Approach 2:
The system performs preliminary actions by pre-defining checkpoints at critical decision points in the execution flow. These checkpoints are established beforehand with known identifiers, allowing the system to trace execution paths without dynamic complexity during runtime processing.
2Loss of information
If the system retains copies of data logs at each checkpoint during execution flow, then the ability to analyze and retrace events is improved, but the memory usage and processing overhead increase
Solution Approach 1:
The patent extracts only the essential tracing information (checkpoint identifier and timestamp) from the full sensor data at each checkpoint. This extraction allows the system to maintain minimal log entries that capture execution flow information without retaining complete data copies, reducing memory usage and processing overhead.
Solution Approach 2:
The system creates simplified copies of data logs that contain only checkpoint metadata (identifier and timestamp) rather than full sensor data. These lightweight copies enable event retracing without the computational burden of storing and processing complete data sets at each checkpoint.
3Reliability
If the system updates data logs for all checkpoints during execution flow, then the completeness of execution tracing is improved, but the processing time and latency increase
Solution Approach 1:
The patent applies local quality by updating data logs selectively at specific checkpoints rather than uniformly across all processing points. Checkpoints are strategically placed at decision boundaries and critical path points, ensuring tracing completeness for important events while minimizing processing overhead at less critical stages.
4Measurement precision
If the system determines latency for each sensor by comparing timestamps, then the accuracy of latency identification is improved, but the computational complexity and processing overhead increase
Solution Approach 1:
The system performs self-service latency analysis by automatically comparing timestamps between consecutive checkpoints for each sensor. This self-contained timestamp comparison mechanism enables accurate latency identification without requiring complex external analysis tools or additional computational overhead beyond the basic timestamp recording already performed at checkpoints.
Data Source
AI summary
To identify sources of data resulting from an execution flow in a robotic device such as an autonomous vehicle, an operating system receives sensor data from various sensors of the robotic device. For each sensor, the system generates a data log comprising an identifier of a first checkpoint associated with that sensor, as well as a first timestamp. The system performs an execution flow on the sensor data from that sensor. The system updates the data log to include an identifier and timestamp for one or more additional checkpoints during the execution flow. The system then fuses results, uses the fused data as an input for a decision process, and causes a component of the robotic device to take an action in response to an output of the decision process. The system may record the action, an action timestamp and the data logs for each sensor in a memory.