DRONOVA
Multi-platform autonomous swarm network
Dronova builds advanced Multi-Platform Autonomous Swarm Networks—distributed perception, mapping, and adaptive action in real time.
Vision
We see fleets not as loosely grouped vehicles but as a single coherent system: continuously learning, tightly synchronized, and able to reshape fields and decisions in milliseconds—whether for mapping, mesh networking, or mission-critical coordination.
Company
Dronova develops intelligent drone swarms with real-time fine-tuning, distributed SLAM, and adaptive decision-making. The stack combines robust mesh communication with state-of-the-art algorithms so the swarm stays accurate and efficient as environments and objectives change.
Why now
Solution
Dronova ties together edge perception, federated-style aggregation, and rapid policy updates so every unit improves the whole. The result is emergent swarm behavior that can tackle objectives no lone vehicle can—search, inspection, monitoring, and dynamic replanning at fleet scale.
Product pillars
Continuous optimization as conditions change; performance stays peaked in volatile environments.
Collaborative 3D mapping and navigation with precision suited to cluttered and GPS-denied spaces.
Low-latency, resilient links for data and coordinated decisions across the fleet.
From light to industrial airframes; collective behaviors for tasks beyond any single drone.
Missions that rewrite themselves when objectives or constraints shift mid-flight.
Technology stack
Companion simulations and figures: Technical gallery
Markets
Rapid area coverage and situational awareness in disasters.
Long-horizon sensing for ecosystems and change detection.
Precision mapping, pest cues, and targeted interventions.
Bridges, lines, pipelines—scalable inspection with 3D context.
Reconnaissance and awareness in complex environments.
Detailed city models for development and mobility.
Proof points
Our flagship demo runs a real autonomous swarm loop: distributed sensing, shared belief mapping, mesh connectivity tracking, Lloyd coverage planning, Hungarian task allocation, collision avoidance, and online adaptation. A validated run reached 0.964 coverage confidence with zero collision-pressure events. Use the technical gallery as the appendix for deep dives.
Team
Founder — Company site
Next step: Schedule a meeting via the company homepage.
Closing
Multi-platform fleets, mesh-native coordination, and learning in the loop. We’re building the aerial intelligence layer for missions that demand scale, resilience, and precision.