Drone Swarm Waypoint Control With Self-Calculated Sortie Paths
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Solution Overview
Problem
Current methods for controlling autonomous drone swarms face challenges in efficiently programming and synchronizing a large number of vehicles, particularly due to the complexity of issuing instructions and the time-consuming process of preparing CSV files for pathway uploads, which limits re-tasking and bandwidth efficiency.
Innovation Solution
A system that allows each drone to calculate its own sequence of pathways and waypoints based on a high-level sortie specification, eliminating the need for extensive CSV file preparation and reducing bandwidth requirements, enabling quick and on-the-fly programming of drone swarms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If a central controller manages all drones, then coordination is simplified, but the complexity of issuing instructions increases exponentially with the number of drones
Solution Approach 1:
The patent divides the swarm into multiple teams, with each team managed by a team leader drone. This segmentation allows the central controller to issue instructions to team leaders rather than individually to each drone, reducing instruction complexity from exponential to linear scaling with team count.
Solution Approach 2:
Team leader drones act as intermediaries between the central controller and individual drones. The central controller communicates mission parameters to team leaders, who then coordinate their team members, reducing the direct communication burden on the central controller.
2Extent of automation
If each drone is programmed individually with local information, then autonomy is improved, but the time required to program each drone increases exponentially
Solution Approach 1:
The patent implements universal programming templates that define standardized behaviors and decision-making protocols for all drones. This allows drones to operate autonomously using common frameworks rather than individual custom programming, dramatically reducing programming time while maintaining autonomy.
Solution Approach 2:
The system pre-defines behavioral templates and decision-making algorithms that drones can directly apply to their local situations. This preliminary preparation eliminates the need for real-time individual programming, allowing drones to autonomously interpret mission parameters using pre-established rules.
3Manufacturing precision
If extensive CSV files are prepared for pathway uploads, then pathway precision is improved, but the bandwidth requirements and preparation time increase significantly
Solution Approach 1:
The patent extracts only the essential mission parameters and high-level pathway information needed for drone navigation, transmitting these compact representations rather than complete detailed CSV pathway files. This maintains sufficient navigation precision while dramatically reducing data transmission bandwidth requirements.
Solution Approach 2:
Instead of uploading complete pathway data to each drone, the system inverts the approach by providing minimal guidance parameters and allowing drones to generate their own detailed pathways locally using onboard computation and pre-loaded navigation algorithms.
Data Source
AI summary
In accordance with an embodiment, a method of operating a vehicle within a vehicle swarm includes: receiving a sortie specification, the sortie specification specifying a desired behavior the vehicle swarm is to perform; obtaining a position identification within the vehicle swarm; and calculating a set of waypoints based on the received sortie specification and the position identification.