Naval Drone Landing Control Adapting to Platform Motion
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Solution Overview
Problem
Existing methods for controlling drone landing and take-off on naval platforms, especially in heavy seas, have not provided full satisfaction due to instability and unpredictability of movements, requiring a more robust and adaptive control strategy.
Innovation Solution
A method that involves acquiring and processing the movements of the naval platform, calculating average and predicted positions, and determining minimum speed to adjust landing and take-off strategies dynamically, including a rendezvous phase, approach phase, and precise piloting using geolocation and optical deviation sensors to ensure safe operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional landing control methods are used on naval platforms, then the system is simple to operate, but the reliability of drone operations deteriorates in heavy seas due to platform instability and unpredictability of movements
Solution Approach 1:
The control system dynamically adapts its behavior based on real-time sea conditions and platform movements. It transitions between different control modes (active compensation when movements are strong, passive following when movements are weak) to maintain reliable drone operations across varying operational environments.
Solution Approach 2:
The system continuously monitors platform movements through sensors and uses this feedback to adjust the drone's landing trajectory in real-time. This closed-loop control enables the system to compensate for platform instability and maintain high reliability even in heavy seas.
2Manufacturing precision
If the drone follows the movements of the grid actively, then the landing precision is improved, but the complexity of tracking and control increases
Solution Approach 1:
The system dynamically adjusts its tracking behavior based on the characteristics of platform movements. For strong, predictable movements, it applies active compensation with full tracking complexity. For weak or unpredictable movements, it switches to simpler passive following, optimizing the trade-off between precision and complexity in real-time.
Solution Approach 2:
The control system changes its operational parameters based on sea conditions and platform movement characteristics. It adjusts tracking gain, compensation intensity, and control aggressiveness to achieve optimal landing precision while adapting the complexity of the tracking system to the actual operational needs.
3Reliability
If the landing strategy adapts to sea conditions, then the reliability in heavy seas is improved, but the complexity of decision-making logic increases
Solution Approach 1:
The control logic dynamically selects appropriate landing strategies based on real-time assessment of sea conditions and platform movements. It transitions between active compensation mode, passive following mode, and hybrid modes to maintain reliability across different operational environments without requiring a completely complex decision-making system for each condition.
4Reliability
If the drone waits for minimum platform speed to land, then the safety is improved, but the operation time increases
Solution Approach 1:
The system performs preliminary assessment of platform movement trends and predicts future minimum-speed windows. By anticipating optimal landing opportunities in advance, it can prepare the drone for landing sooner rather than waiting passively, thereby reducing overall operation time while still ensuring safety through proper timing.
Data Source
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AI summary
The invention relates to method characterised in that said method comprises steps of acquiring movements, calculating the mean position, calculating position predictions, and calculating minimum grid (5) movement speeds, and a step of acquiring the position of the drone (4) so that, if the drone cannot follow the movements of the grid and the movements of the grid are small, i.e. smaller than the radius of the latter, it is possible to apply a landing strategy by monitoring the mean position of the grid, and if the movements of the grid are large, i.e. larger than the radius of the grid, it is possible to apply a landing strategy by positioning at the minimum speeds of the grid, and if the drone (4) can follow the movements of the grid (5) and the movements of the grid are small, i.e. smaller than the radius of the grid, it is possible to apply a landing strategy according to the mean position of the grid, and if the movements of the grid are large, i.e. larger than the radius of the grid, it is possible to apply a landing strategy by following the position of the grid predicted at the instant of landing.