Checkpoint Tracing for Robotic Sensor Flow Latency Analysis
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Solution Overview
Problem
Complex robotic systems like autonomous vehicles face challenges in retracing events due to real-time processing requirements, leading to uncertainty about the data used for decision-making, especially during training and fault diagnosis.
Innovation Solution
A checkpoint-based tracing system that generates data logs with identifiers and timestamps for each sensor, fuses results from execution flows, and identifies latency by comparing action timestamps with thresholds, enabling safe mode operation and corrective actions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If the system processes all sensor data in real-time to maintain current processing, then processing speed is maintained, but the ability to trace and analyze execution flows is lost due to skipped steps and ignored data
Solution Approach 1:
The system performs preliminary actions by inserting checkpoints into the execution flow before real-time processing occurs. These checkpoints capture essential data points, sensor readings, and execution states in advance, ensuring that traceability information is preserved even when real-time processing skips certain steps to maintain speed.
Solution Approach 2:
The system creates copies of critical execution flow data at checkpoints without requiring the original processing steps to be retained. By copying essential information (sensor data, timestamps, execution states) at strategic points, the system maintains traceability while allowing the main processing flow to continue at full speed without being burdened by complete data retention.
2Reliability
If the system records detailed data logs for every sensor at every checkpoint to enable complete tracing, then traceability is improved, but system complexity and data storage requirements increase
Solution Approach 1:
The system applies local quality by implementing selective data logging at specific checkpoints rather than uniformly across all processing points. Each checkpoint is strategically placed to capture essential traceability information for particular execution flows or decision points, recording detailed logs only where necessary for fault diagnosis and training analysis, thereby reducing overall system complexity.
3Loss of information
If the system updates data logs for all checkpoints during execution flow to maintain complete records, then data completeness is improved, but processing overhead and latency increase
Solution Approach 1:
The system implements partial action by updating data logs only for selected checkpoints that are critical for traceability and fault diagnosis, rather than updating every single checkpoint. This selective approach captures sufficient information for analyzing execution flows and identifying latency sources while minimizing the processing overhead and time penalty associated with comprehensive logging.
Data Source
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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.