Checkpoint-Based Sensor Traceability for Robotic Decision Latency
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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 volume processing, which can lead to uncertainty about the data used for decision-making and latency issues.
Innovation Solution
An automated device monitoring system that generates data logs with timestamps for each sensor, performs execution flows, fuses results, and uses the fused data for decision-making, while tracing latency by identifying checkpoints and recording actions to facilitate real-time analysis and fault diagnosis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If the system processes and logs all sensor data points in real-time, then complete data tracking is achieved, but processing latency increases and real-time performance deteriorates
Solution Approach 1:
The patent segments the data logging process by creating separate data structures for different types of information (sensor data, execution flow data, decision data, action data) and only logging specific segments at specific checkpoints rather than all data continuously. This selective segmentation reduces overall logging overhead while maintaining traceability of critical decision-making data.
Solution Approach 2:
The system performs preliminary actions by pre-defining checkpoints at critical stages of the execution flow (sensor data receipt, execution flow processing, decision making, action execution) before actual processing occurs. This allows the system to prepare logging structures in advance and only populate them when necessary, reducing real-time processing latency.
2Reliability
If the system logs detailed execution flow data for every sensor, then traceability is improved, but data volume and processing complexity increase
Solution Approach 1:
The patent applies local quality by implementing different logging strategies for different parts of the system. Critical components (sensors, execution flows, decisions, actions) have detailed logging at specific checkpoints, while non-critical components use simplified or no logging. This localized approach maintains traceability where needed without uniformly increasing system-wide complexity.
Solution Approach 2:
The system uses a universal data structure framework that can accommodate multiple types of data (sensor readings, execution flow states, decision outputs, action commands) in a consistent format. This multi-functional logging structure simplifies processing complexity by providing a unified approach to handling diverse data types across different system components.
3Loss of information
If the system updates data logs at every checkpoint, then complete execution tracking is achieved, but memory usage and processing overhead increase
Solution Approach 1:
The system applies partial action by updating data logs only at specific checkpoints rather than continuously throughout the entire execution flow. Critical checkpoints (data receipt, processing start, decision point, action execution) are logged, while intermediate steps use reference to parent checkpoint data. This partial logging approach maintains sufficient execution tracking while significantly reducing processing overhead and memory usage.
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.