Multi-Drone Fleet Control With STL Trajectory Robustness
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Solution Overview
Problem
Current methods for controlling multi-drone fleets lack expressiveness to capture complex spatial, temporal, and reactive requirements, often relying on simplifying abstractions that result in conservative behavior and are computationally intractable, failing to provide real-time guarantees for continuous-time system behavior.
Innovation Solution
The use of Signal Temporal Logic (STL) to generate robust and dynamically feasible trajectories for quadrotors, ensuring that the drones satisfy mission specifications by maximizing smooth robustness and using a mapping between low-rate and high-rate trajectories, while respecting velocity and acceleration constraints.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If existing planning methods are used for multi-drone control, then computational tractability is improved, but the expressiveness to capture complex spatial, temporal, and reactive requirements deteriorates
Solution Approach 1:
The patent introduces Signal Temporal Logic (STL) as an intermediary formalism that bridges the gap between complex mission requirements and computationally tractable control. STL provides a structured language to express spatial, temporal, and reactive constraints, enabling the system to handle complex requirements without sacrificing computational efficiency. The robustness metric derived from STL serves as a mediator that quantifies satisfaction of these complex constraints in a computationally manageable way.
Solution Approach 2:
The patent transforms the control problem by changing parameters from discrete planning variables to continuous trajectory parameters. By formulating the problem in continuous time and space with STL constraints, the system can leverage efficient continuous optimization methods while maintaining the ability to express complex requirements. The robustness maximization approach changes the optimization parameter from binary satisfaction to continuous robustness margins, improving computational tractability.
2Device complexity
If simplifying abstractions are used in planning methods, then computational complexity is reduced, but the behavior becomes overly conservative
Solution Approach 1:
The patent applies partial action by focusing optimization on critical subsets of constraints rather than all possible constraints simultaneously. The robustness maximization approach selectively emphasizes constraints that most impact safety and mission success, allowing the system to achieve good enough behavior without exhaustive analysis of all possible scenarios. This partial focus reduces computational complexity while avoiding overly conservative behavior across all constraints.
Solution Approach 2:
The patent introduces dynamics by using continuous-time trajectories instead of discrete planning steps. This allows the system to adapt its behavior continuously rather than following rigid pre-planned paths. The dynamic trajectory optimization enables real-time adjustments that balance computational efficiency with flexible, non-conservative behavior, as the system can react to changing conditions without re-planning entire trajectories.
3Ease of manufacture
If existing methods are used for multi-drone control, then implementation simplicity is improved, but real-time guarantees for continuous-time system behavior are lost
Solution Approach 1:
The patent substitutes traditional discrete-event planning mechanisms with continuous-time control methods. By replacing discrete planning algorithms with continuous trajectory optimization based on STL robustness, the system maintains implementation simplicity while gaining real-time guarantees. The continuous formulation naturally accommodates real-time execution requirements, as trajectories are defined in continuous time and can be evaluated at any moment during flight.
4Productivity
If low-rate trajectory optimization is used, then computational speed is improved, but trajectory accuracy may deteriorate
Solution Approach 1:
The patent applies preliminary action by performing robustness maximization on a coarse low-rate trajectory first, then using the result to guide high-rate trajectory generation. This two-stage approach allows computational speed improvement from low-rate optimization while ensuring final trajectory accuracy through the mapping to high-rate trajectories. The low-rate optimization provides a preliminary solution that constrains the search space for subsequent high-rate refinement, maintaining both speed and accuracy.
Solution Approach 2:
The patent resolves the accuracy-speed tradeoff by adding a temporal dimension to the optimization. Instead of optimizing trajectory points at a single rate, the system optimizes across multiple time scales - low-rate for overall mission planning and computational speed, high-rate for detailed trajectory accuracy. This multi-scale temporal dimension allows the system to achieve both computational efficiency and trajectory precision by operating at appropriate rates for different phases of trajectory generation.
Data Source
AI summary
Methods, systems, and computer readable media for controlling a fleet of drones. A method includes receiving a mission specification for each drone of one or more drones, each mission specification including spatio-temporal requirements for the drone. The method includes generating, for each drone, a low-rate trajectory for the drone by performing a robustness maximization of satisfying the mission specification over a low-rate sequence of waypoints for the drone based on a mapping between low-rate trajectories and high-rate trajectories. The method includes transmitting, to each drone, the low-rate trajectory for the drone, causing a local controller of each drone to control the drone by generating a high-rate trajectory using the low-rate trajectory and the mapping between low-rate trajectories and high-rate trajectories.