UAV Mission Trajectory Optimization Under Tasking Constraints
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Solution Overview
Problem
Current UAV payload tasking methods are inefficient due to reliance on heuristics and simplifications, leading to suboptimal mission value and increased complexity in determining optimal task sequences under physical and timing constraints.
Innovation Solution
The method integrates dynamic optimization to generate an optimal tasking sequence and mission trajectory for UAVs by treating the tasking problem as a single continuous-time problem, eliminating the need for heuristics and simplifications, and using label space representations to embed combinatorial variables within continuous-time calculus.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If heuristics and simplification steps are used to reduce problem complexity, then the computational complexity is reduced to manageable levels, but the solution fidelity and mission value are degraded
Solution Approach 1:
The patent segments the complex hybrid dynamic optimization problem into a continuous-time optimal control problem by separating the combinatorial task sequencing from the continuous trajectory optimization. This is achieved by formulating the problem in terms of continuous time variables and using optimal control theory, which allows the problem to be solved without discrete heuristics while maintaining solution fidelity.
Solution Approach 2:
The patent replaces the mechanical/heuristic approach of iterative simplification with a mathematical substitution by using continuous-time calculus and optimal control theory. Instead of using graph-theoretic algorithms and heuristics, the invention uses Pontryagin's maximum principle and Hamiltonian dynamics to directly solve for the optimal tasking sequence and trajectory, eliminating the need for iterative approximation.
2Reliability
If multiple iterative loops and simulation tests are used to verify feasibility, then the constraints are satisfied, but the computational time and processing requirements increase
Solution Approach 1:
The patent incorporates constraint satisfaction directly into the problem formulation through the Hamiltonian framework and adjoint equations. By formulating the constraints as part of the optimal control problem from the beginning, the solution inherently satisfies all physical and timing constraints without requiring separate verification loops or iterative testing.
Solution Approach 2:
The patent uses feedback through the adjoint variables and Hamiltonian dynamics to continuously enforce constraints during the optimization process. The costate equations provide real-time feedback on constraint satisfaction, allowing the system to adjust the optimal control inputs to maintain feasibility throughout the trajectory without requiring post-solution verification.
3Ease of manufacture
If the tasking problem is decomposed into separate motion planning and task scheduling sub-problems, then each sub-problem can be solved using specialized algorithms, but the integrated optimization is lost and overall mission value decreases
Solution Approach 1:
The patent merges the separate motion planning and task scheduling sub-problems into a unified continuous-time optimal control problem. By formulating both the trajectory optimization and task sequencing decisions simultaneously within the Hamiltonian framework, the invention captures the interdependencies between motion and scheduling that decomposed approaches miss, thereby maximizing overall mission value.
Solution Approach 2:
The patent creates a universal optimization framework that handles both motion planning and task scheduling within a single mathematical structure. The continuous-time formulation with state variables, control inputs, and adjoint variables provides a multi-functional approach that simultaneously optimizes trajectory, timing, and task selection, eliminating the need for separate specialized algorithms.
Data Source
AI summary
A method and system for generating an optimal trajectory path tasking for an unmanned aerial vehicle (UAV) for collection of data on one or more collection targets by a sensor on the UAV.