Aircraft Flight Parameter Processing for Slung Load Touchdown Detection
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing methods for managing the transition of slung load cargo, particularly in rotorcraft and UAV applications, are costly and prone to multiple points of failure, as they rely on additional sensors and algorithms that do not effectively account for the unique dynamics of slung load situations, leading to potential damage from excessive impact during landing.
Innovation Solution
A method and apparatus that process aircraft flight parameters such as collective input, engine power, and shaft torque to determine changes exceeding a threshold, enabling decoupling of the load from the aircraft, which includes using nonlinear gains and dynamic weights to scale and weigh these parameters, and utilizing altitude or pressure sensors to determine proximity to the ground for safe detachment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If additional sling load sensors and camera optical aids are used to detect cargo touchdown, then the detection accuracy is improved, but the system cost and complexity increase
Solution Approach 1:
The patent extracts the touchdown detection function from dedicated external sensors and relocates it to the aircraft's existing flight control computer by monitoring changes in flight parameters (collective pitch, engine power, shaft torque) that naturally occur during cargo touchdown, thereby eliminating the need for additional detection devices
Solution Approach 2:
The flight control computer, originally designed for flight control, is made multi-functional by enabling it to perform both flight control and cargo touchdown detection through algorithmic processing of existing flight parameter data, eliminating the need for separate dedicated detection systems
2Reliability
If multiple sensors and algorithms are deployed for touchdown detection, then the detection capability is improved, but the number of failure points increases
Solution Approach 1:
The patent removes additional sensors and algorithms from the system, relying instead on the aircraft's existing flight control computer and its monitoring of flight parameters that inherently change during cargo touchdown, thereby reducing failure points while maintaining detection capability
Solution Approach 2:
The flight control computer uses its own existing data (flight parameters) to detect cargo touchdown, making the system self-sufficient without requiring external dedicated sensors, thereby reducing both complexity and failure points
3Measurement precision
If traditional sensor-based methods are used for cargo transition management, then the detection accuracy is improved, but the cost increases
Solution Approach 1:
The flight control computer is made multi-functional to perform both flight control and cargo transition detection using existing flight parameter data, eliminating the need for expensive dedicated sensors and reducing overall system cost
Solution Approach 2:
The patent extracts the detection function from expensive external sensors and implements it within the existing flight control computer through algorithmic processing of flight parameters, thereby maintaining detection accuracy while reducing cost
4Measurement precision
If soft-weight-on-wheels algorithms are used, then the landing detection is improved, but the applicability to slung load situations is reduced
Solution Approach 1:
The patent adapts the detection approach by changing the monitored parameters from weight-on-wheels (fixed-wing) to flight control parameters like collective pitch, engine power, and shaft torque (rotorcraft), making the algorithm applicable to slung load situations while maintaining detection accuracy
Solution Approach 2:
The patent creates a dynamic detection algorithm that monitors changes in flight parameters over time during the cargo delivery process, enabling it to detect slung load touchdown events that differ fundamentally from fixed-wing landing events
Data Source
Figure 1A
Figure 1B
Figure 2
AI summary
Embodiments are directed to obtaining data associated with at least one aircraft flight parameter when an aircraft is being operated in flight; processing the data to determine that the at least one aircraft flight parameter indicates a change in value in an amount greater than a threshold; and decoupling a load from the aircraft based on determining that the at least one aircraft flight parameter indicates the change in value in the amount greater than the threshold.