UAV Flight Planning with 4D Trajectory Obstacle Avoidance
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Solution Overview
Problem
Existing UAV flight planning systems struggle to efficiently compute flight plans that navigate around obstacles with both spatial and temporal dimensions, such as restricted airspaces and weather phenomena, which can lead to infeasible or unattainable trips.
Innovation Solution
The development of algorithms and systems that calculate 3-D and 4-D trajectories for UAVs, incorporating spatial and temporal obstacles, and dynamically reroute flights to avoid these obstacles, including Temporary Flight Restrictions and other time-dependent constraints, using graph theory and aircraft performance models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional flight planning systems are used, then the system complexity is low, but the ability to navigate around temporal and spatial obstacles is insufficient
Solution Approach 1:
The patent extends traditional 3D flight planning to 4D by incorporating temporal dimension. Flight paths are represented as four-dimensional trajectories (x, y, z, t) to navigate around temporal obstacles such as Temporary Flight Restrictions. This dimensional extension enables the system to account for time-dependent constraints while maintaining comprehensive obstacle avoidance capabilities.
Solution Approach 2:
The flight planning system segments the navigation problem into multiple components: spatial obstacle avoidance, temporal obstacle avoidance, and integrated 4D trajectory optimization. By dividing the complex planning task into manageable segments handling different obstacle types separately before integration, the system achieves high adaptability without overwhelming complexity.
2Measurement precision
If 4D trajectories are calculated to account for temporal obstacles, then the navigation accuracy improves, but the computational time increases
Solution Approach 1:
The system performs preliminary calculations of 4D trajectories by pre-computing flight paths that account for anticipated temporal obstacles such as TFRs. By calculating these trajectories in advance before actual flight execution, the system achieves high trajectory accuracy without incurring computational delays during critical flight phases.
Solution Approach 2:
The flight planning system dynamically adjusts computational depth based on flight phases and obstacle types. For routine segments, simplified models are used to reduce computation time, while for critical segments involving complex temporal obstacles, more rigorous 4D trajectory calculations are performed to ensure accuracy.
3Adaptability or versatility
If multiple destinations are visited, then the mission versatility improves, but the flight plan complexity increases
Solution Approach 1:
The patent implements a universal flight planning framework that handles multiple destination visits through a single integrated 4D trajectory optimization process. The system uses a unified cost function that simultaneously considers spatial constraints, temporal obstacles, and mission requirements across multiple destinations, eliminating the need for separate planning procedures for different mission types.
4Reliability
If real-time rerouting is performed to avoid obstacles, then the flight safety improves, but the computational resources required increase
Solution Approach 1:
The flight planning system incorporates self-service capabilities by implementing on-board computational resources that can perform real-time rerouting decisions autonomously. The UAV itself possesses the computational ability to detect obstacles and recalculate 4D trajectories without constant ground station intervention, improving flight safety while distributing computational resource requirements.
Data Source
AI summary
This description provides tools and techniques for computing flight plans for unmanned aerial vehicles (UAVs) while routing around obstacles having spatial and temporal dimensions. Methods provided by these tools may receive data representing destinations to be visited by the UAVs, and may receive data representing obstacles having spatial and temporal dimensions. These methods may also calculate trajectories spatial and temporal dimensions, by which the UAV may travel from one destination to another, and may at least attempt to compute flight plans for the UAVs that incorporate these trajectories. The methods may also determine whether these trajectories intersect any obstacles, and at least attempt to reroute the trajectories around the obstacles. These tools may also provide systems and computer-readable media containing software for performing any of the foregoing methods.