Autonomous Work Machine Exception Handling for False-Positive Events
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Solution Overview
Problem
Autonomous work machines face challenges in effectively handling machine health and job quality issues during operations, which can lead to safety concerns and inefficiencies, as existing systems lack robust exception handling procedures to differentiate between genuine and false positives, and may not provide intuitive interfaces for operators.
Innovation Solution
A computer-implemented method that detects machine health/job quality issues, determines if an exception routine is applicable, executes the routine, and communicates with supervisors to resolve the issue, ensuring continued operation or shutdown based on supervisor input, while documenting and notifying relevant personnel.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If autonomous work machines implement comprehensive exception handling procedures, then safety and operational reliability improve, but system complexity increases
Solution Approach 1:
The exception handling system is segmented into distinct modules: detection module that identifies machine health and job quality issues, evaluation module that assesses whether issues are genuine or false positives, exception routine module that executes predefined responses, and communication module that interfaces with supervisors. This segmentation allows comprehensive safety checks without creating a monolithic complex system.
Solution Approach 2:
Exception routines are predefined and prepared in advance for various potential machine health and job quality issues. When an issue is detected, the system immediately executes the pre-prepared routine rather than deciding in real-time, which maintains high reliability while avoiding the complexity of real-time decision-making algorithms.
2Reliability
If the system executes exception routines immediately upon detecting issues, then operational reliability improves, but productivity decreases due to mission interruptions
Solution Approach 1:
The system applies partial action by selectively executing exception routines only for genuine issues after validation, rather than responding to every detected anomaly. The system performs just enough verification to distinguish genuine problems from false positives, avoiding excessive interruptions to mission execution and maintaining productivity while ensuring reliability.
Solution Approach 2:
The system incorporates feedback loops where exception routine outcomes are evaluated to determine if the issue was genuine or a false positive. This feedback mechanism allows the system to learn from each incident and adjust future responses, improving operational reliability over time while minimizing unnecessary mission interruptions that would reduce productivity.
3Ease of operation
If the system provides detailed interfaces for supervisor decision-making, then ease of operation improves, but device complexity increases
Solution Approach 1:
The system extracts and presents only the essential information needed for supervisor decision-making: the detected issue type, the proposed exception routine, and the expected outcome. By taking out only the critical elements and omitting unnecessary technical details, the interface remains simple and easy to operate while still providing sufficient information for informed decisions.
4Measurement precision
If the system differentiates between genuine and false positives, then measurement precision improves, but device complexity increases
Solution Approach 1:
The system performs preliminary validation checks and cross-references detected issues against multiple data sources before classifying them as genuine or false positives. By conducting these preparatory assessments in advance, the system achieves high measurement precision in issue differentiation without requiring complex real-time analysis algorithms during critical operations.
Data Source
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AI summary
A control system for an autonomous work machine includes a robotic controller, a position detection system coupled to the robotic controller, and a sensor coupled to the robotic controller and configured to provide a sensor signal. A controlled system is coupled to the robotic controller to receive control signals from the robotic controller. The robotic controller is configured to generate an event relative to an object in an environment around the autonomous work machine or a machine health/job quality issue, document the event, and store the documented event. The robotic controller is further configured to selectively generate a communication containing at least some information relative to the documented event to a supervisor and to receive user input from the supervisor and take responsive action based on the user input.