US 12,608,024 B1Granted

Autonomous drone swarm system with AI-driven coordination and multi-modal sensor integration

AssigneeDrone Operations LLC

InventorNicholas Thompson

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Abstract

An autonomous drone swarm system and method utilizing artificial intelligence for coordinated operations comprises command drones equipped with large language model processors and subordinate drones coordinated through a hierarchical Queen-Worker architecture. The system processes natural language commands, generates autonomous mission plans, and coordinates multi-drone operations through an encrypted self-healing mesh communication network utilizing laser, radio frequency, and visual communication channels. Multi-modal sensor integration including electro-optical, infrared, LiDAR and photogrammetry, radio frequency, and chemical detection provides comprehensive environmental awareness while federated learning algorithms enable distributed coordination in signal-denied environments. The system implements configurable operational modes spanning tactical (15 minutes-2 hours over 2 to 5 square kilometers), operational (6-24 hours over 20 to 50 square kilometers), and strategic (7-30+days over 200 to 500 square kilometers) mission profiles for military and commercial applications. Fault-tolerant protocols ensure continued operation despite individual failures through automatic task redistribution and leader election procedures.

Claims18

Dependent claims

An autonomous drone swarm system comprising:

at least one command drone comprising an artificial intelligence processor configured to:
process natural language commands from human operators into structured mission parameters;
autonomously generate mission plans based on environmental analysis and available drone resources;
and
delegate tasks among subordinate drones based on drone capabilities and positioning;
a plurality of subordinate drones, each comprising autonomous processing capabilities for coordinated task execution;
a multi-modal communication system establishing encrypted communication links between drones using at least two different communication modalities to form a self-healing mesh network;
a distributed sensor integration system comprising multiple sensor types across the command drone and subordinate drones providing environmental awareness and target detection capabilities;
hierarchical coordination algorithms wherein:
the command drone processes natural language operator inputs and generates executable mission plans;
specific tasks are automatically assigned to subordinate drones based on mission requirements and drone status;
and
real-time mission adaptation occurs based on environmental changes and operational feedback;
fault-tolerant operation protocols maintaining system coordination despite individual drone failures through automatic task redistribution and dynamic role reassignment;
wherein the artificial intelligence processor implements a large language model architecture configured to interpret voice commands, text instructions, and gesture recognition inputs, and wherein the processor converts high-level operator directives into executable mission parameters through contextual awareness algorithms that analyze current environmental conditions and available swarm resources.

12 more elements

A method for autonomous drone swarm coordination comprising the steps of:

processing natural language commands from human operators into structured mission parameters at a command drone using artificial intelligence algorithms;
autonomously generating mission plans based on environmental analysis and available drone resources;
delegating tasks among a plurality of subordinate drones based on drone capabilities and positioning within a hierarchical command structure;
establishing encrypted communication links between drones using at least two different communication modalities to form a self-healing mesh network;
integrating sensor data from multiple sensor types distributed across the command drone and subordinate drones to provide environmental awareness and target detection capabilities;
implementing hierarchical coordination algorithms wherein:
the command drone processes natural language operator inputs and generates executable mission plans;
specific tasks are automatically assigned to subordinate drones based on mission requirements and current drone operational status;
and
real-time mission adaptation occurs based on environmental changes and operational feedback;
maintaining system coordination despite individual drone failures through automatic task redistribution and dynamic role reassignment;
and
executing assigned tasks autonomously at subordinate drones while maintaining coordination with command drone mission objectives;
wherein processing natural language commands comprises the steps of:
detecting input modality from voice commands, text instructions, or gesture recognition inputs;
implementing large language model processing to interpret high-level operator directives;
analyzing current environmental conditions and available swarm resources through contextual awareness algorithms;
and
converting operator directives into executable mission parameters based on operational context and resource availability.

16 more elements

Dependencies are inferred from the claim text.