Aircraft Cargo Handling Autonomy Control Under Sensor Failure
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Solution Overview
Problem
Autonomous cargo handling systems in aircraft face challenges in dynamically adjusting autonomy levels based on sensor and actuator performance, as well as human presence, to ensure safe and efficient operation.
Innovation Solution
A method and system that utilize a processor to adjust autonomy levels by receiving sensor and actuator databases, performing confidence and calibration assessments, and reducing autonomy levels from full or semi-autonomous modes to discrete or manual modes when sensing agents or actuators fail or when humans are detected in the aircraft envelope.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If the system operates at a higher level of autonomous control, then productivity and efficiency are improved, but safety and reliability deteriorate when sensor failures or human presence occur
Solution Approach 1:
The system dynamically adjusts the autonomy level based on real-time sensor performance and human presence detection. The processor monitors sensor databases and confidence levels, automatically transitioning between full-autonomous, semi-autonomous, and manual modes to optimize both productivity and safety according to current operating conditions.
Solution Approach 2:
The system implements continuous feedback loops where sensors monitor the environment and system state, the processor evaluates confidence levels based on sensor data quality, and the autonomy level is adjusted accordingly. This feedback mechanism ensures safety while maintaining high productivity when conditions permit.
2Reliability
If the system reduces autonomy level to lower levels, then safety and reliability are improved, but productivity and efficiency deteriorate
Solution Approach 1:
Rather than operating at a fixed low autonomy level, the system dynamically transitions between autonomy levels. When safety concerns arise (sensor failures or human presence), the system reduces autonomy; when conditions are favorable, it increases autonomy to maximize productivity, thus avoiding the continuous productivity loss that would result from permanently operating at low autonomy levels.
Solution Approach 2:
The system changes the autonomy parameter based on confidence levels derived from sensor data quality. By adjusting this critical parameter dynamically, the system can operate at high productivity levels when sensor confidence is high, and switch to safer lower autonomy levels when confidence decreases, optimizing the trade-off between productivity and safety.
3Ease of operation
If the system operates with full autonomy, then ease of operation is improved, but device complexity increases due to multiple sensing agents and actuators
Solution Approach 1:
The cargo handling system performs self-monitoring and self-adjustment of autonomy levels based on sensor data and system state. The processor automatically evaluates confidence levels and transitions between operational modes without requiring constant human intervention or complex external control systems, thereby maintaining ease of operation despite the underlying system complexity.
Solution Approach 2:
The system divides the autonomous operation into discrete autonomy levels (full-autonomous, semi-autonomous, manual). This segmentation allows the complex system to be managed through simplified mode transitions, making operation easier by providing clear, discrete operational states rather than requiring continuous complex decision-making.
4Reliability
If the system continuously monitors sensor and actuator performance, then reliability is improved, but use of energy increases
Solution Approach 1:
The system changes the intensity of monitoring based on operational needs and confidence levels. Rather than continuously analyzing all sensor data at maximum processing intensity, the processor adjusts monitoring and evaluation depth dynamically, maintaining reliability while reducing energy consumption during periods of high confidence or stable operation.
Data Source
AI summary
A method for adjusting a system autonomy level of a cargo handling system configured for autonomous control by a processor is disclosed. In various embodiments, the method includes receiving by the processor a sensor database from a plurality of sensing agents in operable communication with the processor; determining by the processor a confidence level based on the sensor database; and adjusting by the processor the system autonomy level for continued operation of the cargo handling system.