Dynamic Management System for Adaptive Drone Swarm Control
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Solution Overview
Problem
Conventional drone-swarm systems lack the ability to dynamically manage the creation, maintenance, and termination of drone swarms, failing to adapt to user requirements, environmental conditions, and the collective state of the swarm, limiting their applications in dynamic missions and uncertain environments.
Innovation Solution
A dynamic management system for drone swarms that includes a mission receiving device, a drone and pattern recruitment device, a flocking goal device, a drone swapping device, a contextual resolution-assessing device, and a changing device, which collectively enable the recruitment, arrangement, and adaptive maintenance of drones to satisfy user-defined missions and environmental conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional drone systems operate with pre-defined locations and single use, then device complexity is reduced, but adaptability to dynamic missions and environmental conditions deteriorates
Solution Approach 1:
The system implements dynamic management of drone swarms where drones can be recruited, deployed, and terminated based on real-time mission requirements and environmental conditions. The management system continuously adapts the swarm configuration rather than using fixed pre-defined locations, enabling the system to respond dynamically to changing conditions while maintaining coordinated operation through centralized control algorithms.
Solution Approach 2:
The drone swarm management system provides multi-functional capabilities by enabling the same swarm to perform various missions including surveillance, delivery, and environmental monitoring. The system can dynamically reconfigure the swarm for different task types, making a single system capable of executing multiple functions rather than requiring specialized systems for each mission type.
2Reliability
If drone swarms dynamically adapt to environmental conditions and collective state, then mission execution effectiveness is improved, but device complexity increases
Solution Approach 1:
The management system continuously monitors environmental conditions, drone individual states (power reserves, capabilities, wear and service life), and collective swarm state. This feedback information is used to dynamically adjust mission assignments, reconfigure swarm formations, and terminate missions when conditions deteriorate, thereby maintaining high mission execution effectiveness through adaptive decision-making based on real-time system state.
Solution Approach 2:
The system dynamically changes operational parameters including swarm size, mission duration, and drone allocation based on monitored conditions. When environmental conditions or drone states change, the system adjusts these parameters to optimize mission success probability, such as reducing swarm size when power reserves are low or extending mission duration when conditions are favorable.
3Productivity
If conventional systems use fixed mission durations and locations, then ease of operation is maintained, but productivity for uncertain duration missions deteriorates
Solution Approach 1:
The system performs preliminary assessment of environmental conditions and drone capabilities before mission execution. Based on this preliminary analysis, the management system pre-configures appropriate swarm sizes and mission parameters, enabling efficient execution of uncertain duration missions without requiring complex real-time adjustments during critical phases.
Data Source
AI summary
A method, system, and recording medium including a drone and pattern recruiting device configured to recruit a plurality of drones based on a mission, a flocking goal device configured to arrange the plurality of drones in the drone-swarm in a pattern to satisfy the mission, and a changing device configured to adaptively change the pattern of the drone-swarm based on a condition of the mission indicating a needed change and to cause the drone and pattern recruiting device to recruit an additional drone for the needed change.