UAS 4D Trajectory Planning Under Airspace, Wind, and RF Constraints
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Solution Overview
Problem
Current flight planning systems for unmanned aerial systems (UAS) struggle to compute cost-optimized trajectories that simultaneously consider energy constraints, risk levels, restricted airspace, sensor requirements, and variable wind patterns, while ensuring adequate communication and adherence to ground routes, which are critical for efficient and safe UAS operations.
Innovation Solution
A flight-processor apparatus capable of calculating 4-dimensional trajectories that minimize operator-defined costs by incorporating energy, risk, and communication constraints, using numerical methods and mesh-based decompositions to optimize flight paths, including altitude, wind vector data, and geo-fencing, while integrating multiple cost functions to meet various thresholds.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If constant low-altitude AGL flight is used to accommodate sensor proximity and airspace regulations, then sensor performance requirements are satisfied and airspace compliance is maintained, but energy efficiency deteriorates due to frequent contingencies and inability to leverage favorable wind patterns
Solution Approach 1:
The system dynamically adjusts flight altitude between constant low-altitude AGL flight (for sensor performance and compliance) and variable altitude flight (for energy efficiency). The flight processor apparatus computes optimal altitude profiles that transition between these modes based on wind conditions, energy state, and mission requirements, resolving the contradiction between maintaining sensor performance and improving energy efficiency.
Solution Approach 2:
The system changes the altitude parameter from constant to variable based on computational optimization. By allowing altitude to vary within constrained bounds, the system can exploit favorable wind patterns at higher altitudes while maintaining compliance with minimum altitude requirements, thus improving energy efficiency without sacrificing sensor performance or airspace compliance.
2Reliability
If multiple cost constraints (energy, risk, communication, sensor) are simultaneously enforced, then trajectory compliance and safety are improved, but computational complexity increases
Solution Approach 1:
The computational problem is segmented into multiple independent cost function evaluations. Each constraint (energy, risk, communication, sensor) is evaluated as a separate cost function, and the trajectory optimization integrates these segmented costs. This segmentation allows the system to enforce multiple constraints simultaneously while managing computational complexity through modular processing.
Solution Approach 2:
The flight processor apparatus uses a universal cost function framework that can accommodate multiple different constraints through a single integrated optimization process. The same numerical methods and mesh-based decomposition techniques are applied regardless of which specific constraints are active, providing a multi-functional solution that handles energy, risk, communication, and sensor constraints uniformly.
3Use of energy by moving object
If variable altitude flight is implemented to optimize energy consumption, then energy efficiency improves, but compliance with minimum altitude requirements and sensor proximity constraints becomes more difficult to maintain
Solution Approach 1:
The system performs preliminary computation of the optimal altitude profile before execution. The flight processor apparatus calculates the energy-optimized variable altitude trajectory in advance, ensuring that minimum altitude requirements and sensor proximity constraints are built into the computed solution. This preliminary action ensures compliance is maintained while achieving energy optimization.
Solution Approach 2:
The system uses feedback from the computational optimization process to adjust the variable altitude profile. The mesh-based decomposition and numerical methods provide feedback on constraint satisfaction at each computational step, allowing the system to refine the altitude profile to maintain compliance with minimum altitude and sensor proximity requirements while optimizing energy consumption.
Data Source
AI summary
A flight processor that calculates a 4-dimensional trajectory having a sequence of two or more position, time and cost (x, y, z, t, c) tuples that minimize a defined cost. Some embodiments generate cost-optimized trajectories with simple or complex constraints and bounds such as fixed AGL altitude; minimum AGL altitude; maximum AGL altitude; minimum MSL; maximum MSL; avoidance of restricted airspace; adherence to non-restricted airspace such as easements; adherence to ground-based guideways, if applicable; and the constraint to maintain adequate radio frequency signal-to-noise needed for communications to the ground station or backhaul systems. Constraint-enabled minimization of trajectory cost may leverage the aircraft's energy model; current atmospheric data (most notably wind vector data along the trajectory path); continuous-time and/or event-based risk models and fault trees; blacklisted and white-listed geo-fence boundaries; defined easements; and known or estimated RF signal-to-noise (SNR) minimum values needed for one or two-way communications.