Control Normalization for Dissimilar UAV Types
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Solution Overview
Problem
Current systems do not enable a pilot trained to manually fly one type of autonomous vehicle to take control of a very different type of autonomous vehicle without extensive training or certification, as they lack the ability to translate control inputs between dissimilar UAV types, leading to safety and operational challenges.
Innovation Solution
An autonomous vehicle override control system that translates manual control commands from a certified UAV type into appropriate override commands for a different UAV type, using control models and simulation to ensure safe and consistent operation, while also adjusting for the pilot's experience and current conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a pilot is trained to manually fly one type of autonomous vehicle, then they can safely operate that specific vehicle type, but they cannot operate very different types of autonomous vehicles without extensive additional training and certification
Solution Approach 1:
The patent introduces a control normalization system that acts as an intermediary between the pilot's familiar control interface and the target UAV's native control system. This system translates control inputs from one UAV type's control model to another UAV type's control model, enabling pilots to operate different UAV types without extensive retraining while maintaining safety and control accuracy.
Solution Approach 2:
The system dynamically adjusts control parameters by obtaining control models for both the familiar UAV type and target UAV type, then calculating normalization transformations between them. This allows the control system to adapt parameters such as control surface deflections, throttle responses, and flight envelope limits to match the target UAV's characteristics while preserving the pilot's intended actions.
2Ease of operation
If control inputs are directly transmitted between different UAV types, then operational simplicity is maintained, but safety and control accuracy are compromised due to different handling characteristics
Solution Approach 1:
The control normalization system serves as a safety intermediary that translates and validates control inputs between different UAV control models. It ensures that pilot inputs are appropriately transformed to match the target UAV's handling characteristics, preventing unsafe or erroneous control actions while maintaining operational simplicity for the pilot.
Solution Approach 2:
The system incorporates feedback mechanisms by calculating the relationship between control inputs and expected physical movements for both UAV types. This allows the system to verify that translated control commands produce appropriate and safe responses in the target UAV, enhancing reliability while preserving ease of operation.
3Reliability
If extensive training and certification are required for pilots to operate different UAV types, then flight safety is ensured, but operational efficiency and response time are reduced
Solution Approach 1:
The control normalization system enables pilots to immediately operate different UAV types without extensive retraining by providing automatic control translation. This intermediary system maintains flight safety through accurate control model transformations while dramatically improving operational efficiency by eliminating lengthy certification processes for different UAV types.
4Measurement precision
If control models are customized for each specific UAV type, then control accuracy is maximized, but system complexity and data requirements increase
Solution Approach 1:
The system implements a universal control normalization framework that can handle multiple UAV types through a common translation architecture. By obtaining control models for different UAV types and calculating normalization transformations between them, the system maintains high control accuracy for each specific UAV type while avoiding the need for separate custom systems for each aircraft, thus managing complexity through reusability.
Data Source
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AI summary
Methods, systems, and process-readable media include an autonomous vehicle override control system that receives override commands from a pilot qualified on a first type of unmanned autonomous vehicle (UAV) and translates the inputs into suitable commands transmitted to a target UAV of a second UAV type. A pilot's certification for a first UAV type may be determined from the pilot's login credentials. The system may obtain a first control model for the first UAV type and a second control model for the target UAV. Pilot input commands processed through the first control model may be used to calculate movements of a virtual UAV of the type. The system may estimate physical movement of the target UAV similar to the first physical movement, and generate an override command for the target UAV using the second control model and the second physical movement. Control models may accommodate current conditions and pilot experience.