Adaptive UAV Wing Positioning for Energy-Efficient Flight
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Solution Overview
Problem
Current unmanned aerial vehicle (UAV) systems lack efficient optimization of flight patterns and wing positions to minimize energy consumption and maximize task performance in varying environmental conditions, leading to suboptimal task execution and increased operational costs.
Innovation Solution
A system comprising a UAV with motors, adjustable wings coupled via an actuator, and a sensor system, controlled by a central computer system that retrieves task profiles and condition parameters to determine optimal wing positions and motor states for efficient flight, allowing the UAV to adjust its flight mode and wing configuration in real-time based on environmental conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Use of energy by moving object
If the UAV uses fixed flight patterns and wing positions, then the device complexity is reduced, but the energy consumption increases and task performance decreases in varying environmental conditions
Solution Approach 1:
The patent implements dynamic adjustment of wing positions and flight patterns based on real-time environmental conditions. The wing position is no longer fixed but varies dynamically in response to wind speed, wind direction, and temperature changes, allowing the UAV to optimize energy consumption while adapting to changing conditions.
Solution Approach 2:
The system incorporates sensor feedback mechanisms that continuously monitor environmental conditions and feed this information back to the flight control system. This feedback loop enables the UAV to adjust its flight pattern and wing position in real-time, optimizing energy efficiency while maintaining task performance across varying environmental conditions.
2Productivity
If the UAV adjusts flight patterns and wing positions dynamically, then task performance is optimized, but the control system complexity increases
Solution Approach 1:
The control system dynamically adjusts flight patterns and wing positions based on real-time environmental data. The wing position transitions from a static parameter to a dynamic one that responds to wind conditions, temperature, and task requirements, thereby optimizing task execution efficiency while managing control complexity through adaptive algorithms.
Solution Approach 2:
The system changes flight control parameters (wing position, flight pattern) in response to environmental parameter changes. By monitoring wind speed, wind direction, and temperature, the system adjusts corresponding flight parameters to optimize task performance, transforming static control parameters into adaptive variables that respond to environmental conditions.
3Adaptability or versatility
If the UAV uses simplified flight control, then the device complexity is reduced, but the adaptability to varying environmental conditions deteriorates
Solution Approach 1:
The system employs feedback mechanisms where sensors continuously monitor environmental conditions (wind speed, wind direction, temperature) and feed this data to the flight control system. This feedback enables the UAV to adapt to varying environmental conditions by adjusting its flight pattern and wing position, enhancing environmental adaptability while managing system complexity through structured control loops.
Solution Approach 2:
The flight control system changes operational parameters (wing position, flight pattern selection) in response to environmental parameter variations. By linking environmental parameter monitoring to flight parameter adjustment, the system achieves versatile environmental adaptation without requiring overly complex control architecture, as the parameter changes are directly tied to measurable environmental conditions.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This solution enables the UAV to optimize energy consumption and task performance by dynamically adjusting flight patterns and wing positions, enhancing the efficiency and effectiveness of task execution while reducing operational costs.
Implementation Method 1
detect condition parameters of the unmanned aerial vehicle based on the sensor system
Implementation Method 2
cause the set of motors to lift the unmanned aerial vehicle
Implementation Method 3
determine a position for the set of wings based on the task profile and the condition parameters
Data Source
AI summary
Systems, apparatuses, and methods are provided herein for unmanned flight optimization. A system for unmanned flight comprises a set of motors configured to provide locomotion to an unmanned aerial vehicle, a set of wings coupled to a body of the unmanned aerial vehicle via an actuator and configured to move relative to the body of the unmanned aerial vehicle, a sensor system on the unmanned aerial vehicle, and a control circuit. The control circuit being configured to: control the unmanned aerial vehicle, cause the set of motors to lift the unmanned aerial vehicle, detect condition parameters based on the sensor system, determine a position for the set of wings based on the condition parameters, and cause the actuator to move the set of wings to the wing position while the unmanned aerial vehicle is in flight.