UAV Flight Path Planning Using Bird Trajectory Data
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Solution Overview
Problem
Existing flight path planning for UAVs in metropolitan areas with high-rise obstructions and wind effects is inefficient, leading to potential collisions and increased energy consumption, as current methods rely on manual control, environmental sensors, or inefficient algorithms.
Innovation Solution
Using racing pigeons equipped with recording devices to record their flight paths, which are then analyzed to determine the most obstruction-free and efficient flight path, allowing UAVs to autonomously follow these paths and avoid collisions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If UAVs fly in metropolitan areas with high-rise buildings, then they can access urban destinations, but they face obstruction collisions and wind effects
Solution Approach 1:
The system performs preliminary flight path planning by analyzing 3D building models and environmental data before UAV flight. Multiple candidate paths are pre-calculated considering obstructions and wind patterns, allowing the UAV to select the safest route in advance rather than reacting to obstructions during flight.
Solution Approach 2:
The patent introduces an intermediary flight path planning system that acts as a mediator between the UAV and urban obstructions. This system uses 3D building models, wind data, and path algorithms to create intermediate flight routes that automatically navigate around high-rise buildings and obstructions, eliminating the need for manual intervention.
2Reliability
If UAVs manually avoid obstructions, then they can navigate around obstacles, but users must remain within limited connection range
Solution Approach 1:
The flight path planning system enables self-service navigation by automatically calculating and adjusting flight paths around obstructions using pre-loaded 3D building models and real-time environmental data. The UAV independently identifies and avoids obstacles without requiring continuous manual control, allowing users to operate from beyond line-of-sight distances.
Solution Approach 2:
The patent replaces the mechanical manual control system with an automated computational system. Instead of users manually manipulating controllers to avoid obstructions, the system uses algorithms that process 3D building models, wind data, and sensor information to automatically generate collision-free flight paths, substituting human mechanical control with intelligent automation.
3Reliability
If UAVs fly above high-rise buildings, then they can avoid obstructions, but energy consumption and flight time increase
Solution Approach 1:
The system dynamically changes flight path parameters by calculating multiple candidate routes with varying altitudes, distances, and trajectories. Instead of always flying above buildings, the algorithm selects optimal paths that balance altitude requirements with energy efficiency, adjusting parameters based on real-time wind data and building configurations to minimize energy consumption while maintaining safe clearance from obstructions.
4Reliability
If environmental sensors are installed on UAVs, then they can detect obstructions, but flight path planning efficiency decreases
Solution Approach 1:
The system performs preliminary analysis of 3D building models and environmental data before flight to pre-calculate flight paths and identify potential obstructions. This preliminary action eliminates the need for complex real-time sensor processing during flight, as the majority of path planning is completed in advance using detailed building models and environmental information.
Solution Approach 2:
Instead of relying solely on onboard sensors during flight, the system performs excessive analysis by pre-processing comprehensive 3D building models and environmental data to create detailed flight path plans. This partial pre-processing of obstruction information reduces the computational burden on onboard sensors and processors during actual flight, improving overall planning efficiency.
Data Source
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AI summary
A method for flight path planning of unmanned aerial vehicles using flying routes of birds includes: recording multiple pieces of flight data, wherein multiple recording devices (10) are used to record the multiple pieces of flight data when the birds fly from a first designated point to a second designated point and are respectively installed on the birds; generating an optimal flight path, wherein an analyzing device (20) collects the multiple pieces of flight data and calculates the optimal flight path; and controlling a UAV (30) to fly according to the optimal flight path, wherein the optimal flight path is inputted to the UAV (30). By virtue of bird's nature automatically avoiding obstruction and adapting to wind direction and air flow, multiple obstruction-free recording points between two places can be acquired to form an optimal flight path with the shortest flying time or distance.