Autonomous UAV Flight Control for Real-Time Conflict Resolution
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Solution Overview
Problem
Unmanned aerial vehicles (UAVs) are limited by the need for continuous human input for flight control, particularly in dynamic environments, and lack autonomy to resolve conflicts or failures, restricting their use in commercial operations and manned airspace.
Innovation Solution
An autonomous UAV system with an onboard flight controller using machine learning algorithms to dynamically adapt flight plans, incorporating real-time internal and external data from diverse sensors to prioritize conflict resolution and ensure safe operation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If continuous human input is used for flight control, then safety and responsiveness are maintained, but operational complexity and cost increase
Solution Approach 1:
The UAV performs self-service through autonomous conflict detection and resolution capabilities. The onboard flight controller automatically monitors flight parameters, detects potential conflicts using sensor data, calculates multiple flight plan amendments, and implements safe resolution without continuous human intervention. This self-service approach maintains safety while reducing operational complexity.
Solution Approach 2:
The system employs continuous feedback loops where sensor data from the environment and flight systems are fed back to the flight controller in real-time. This feedback enables the UAV to dynamically adjust its flight plan based on current conditions, maintaining safety through automated monitoring and response while eliminating the need for constant human input.
2Productivity
If autonomous conflict resolution is implemented, then operational flexibility and productivity increase, but system complexity increases
Solution Approach 1:
The autonomous conflict resolution system is segmented into distinct functional modules: sensor data acquisition, conflict detection algorithms, flight plan amendment calculation, and implementation control. This segmentation allows each module to perform its specific function efficiently while working together as an integrated system, improving productivity without overwhelming complexity.
Solution Approach 2:
The system dynamically adapts flight plans based on real-time conditions by calculating multiple amendment options and selecting the safest resolution. This dynamic capability enables the UAV to respond flexibly to changing environments and conflicts, significantly improving operational efficiency and productivity.
3Measurement precision
If machine learning algorithms are used for conflict resolution, then decision-making accuracy improves, but computational requirements and processing time increase
Solution Approach 1:
The system performs preliminary calculations by pre-processing sensor data and pre-calculating multiple flight plan amendment options before conflicts become critical. This preliminary action allows machine learning algorithms to process information more efficiently, improving decision accuracy while minimizing processing time during actual conflict resolution.
Solution Approach 2:
When conflicts are detected, the system prioritizes rapid processing by skipping non-critical computations and focusing computational resources on calculating safe flight plan amendments. This approach allows machine learning algorithms to deliver accurate decisions within tight time constraints by ruthlessly prioritizing critical calculations.
Data Source
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AI summary
An autonomous unmanned aerial vehicle (10) comprising an airframe body; at least one flight system mounted to the airframe body(12); an onboard flight controller (18) which is adapted to control the or each flight system; a memory storage unit having machine-readable flight control instructions which are implementable by the onboard flight controller; an onboard feedback system which is communicatively coupled with the or each flight system to provide real-time internal flight characteristic data to the onboard flight controller(18); and an external feedback system adapted to receive and provide to the onboard flight controller (18) real-time external flight characteristic data; wherein the onboard flight controller (18) is arranged to receive mission parameter data from an external source, determine a pre- take-off flight plan in accordance with the mission parameter data, and dynamically implement the machine-readable flight control instructions to adapt the pre-take-off flight plan to control the or each flight system based on the real-time internal flight characteristic data and real-time external flight characteristic data.