| Patent holder | Ceballos Melo Andre Augusto |
| Patent link | View patent |
| Category | Protection & compliance › Wildfire |
Artificial intelligence and swarm intelligence method and system in simulated environments for autonomous drones and robots for suppression of forest fires
Swarm intelligence applied to autonomous drones and ground robots for forest fire suppression represents a cutting-edge convergence of multi-agent AI, simulation-based training, and emergency robotics. The underlying concept draws on the behaviour of biological swarms — such as ant colonies or flocking birds — to coordinate large numbers of relatively simple agents toward a complex shared goal without centralised control. In a forest fire suppression context, individual drones and robots are programmed with local decision rules (e.g., move toward fire, avoid collisions, share sensor data) that collectively produce coordinated firefighting behaviour. Simulated environments, often built on platforms such as ROS/Gazebo, Unreal Engine, or custom physics engines, are used to train and validate these swarm algorithms before deployment in real-world conditions, dramatically reducing the cost and danger of live testing. Each agent typically carries onboard sensors — thermal cameras, gas detectors, LiDAR — and communicates via mesh networking to maintain a shared situational picture of fire spread, terrain, and resource status. Reinforcement learning is frequently used to optimise swarm policies, allowing agents to adapt to dynamic fire behaviour driven by wind, fuel load, and topography. For Australian forestry, where catastrophic bushfire seasons such as 2019–20 have underscored the limits of conventional suppression capacity, autonomous swarm systems offer particular promise: they can operate continuously without fatigue, access terrain too steep or dangerous for ground crews, and be pre-positioned in high-risk plantation or native forest zones. Research groups in Australia, Europe, and the United States have filed patents and published studies on drone-mounted nozzle systems, fire-line mapping UAV swarms, and heterogeneous robot teams combining aerial scouts with ground-based suppressants. The technology remains largely in prototype and trial phases, but integration with national fire management information systems (such as Australia's AFAC data frameworks) is an active area of development.
Not a productivity tool but highly suitable for wildfire-risk reduction, as its primary purpose is protection and early hazard response.
| Field | Value |
|---|---|
| Company | Ceballos Melo Andre Augusto |
| Type | Patent |
| Year | 44686 |
| Status | Pending |
| FWPA RD&E | 2.1 |
| Inventors | Ceballos Melo Andre Augusto |
| Patent No. | US 3023/0064973 A1 |