Swarm intelligence applies to search and rescue (SAR) operations by enabling decentralized groups of robots or drones to collaborate autonomously, mimicking the collective behavior of natural systems like insect swarms. Instead of relying on a central controller, individual agents follow simple rules to share data, adapt to dynamic environments, and cover large areas efficiently. For example, a swarm of drones can split a disaster zone into sectors, search in parallel, and relay findings to a human team. This approach improves scalability and resilience, as the system continues functioning even if some agents fail or lose communication.
Specific applications include area exploration, target identification, and resource coordination. Swarm agents use algorithms like ant colony optimization (ACO) to leave "digital pheromones"—shared markers that guide others toward high-priority zones, such as areas with heat signatures or structural damage. Particle swarm optimization (PSO) helps drones adjust their flight paths in real time based on neighboring agents’ movements and sensor data. In a collapsed building scenario, ground robots might form a communication mesh to penetrate rubble while aerial drones map escape routes. These systems often combine heterogeneous agents, such as unmanned aerial vehicles (UAVs) for aerial surveys and unmanned ground vehicles (UGVs) for closer inspection, all sharing data via decentralized protocols like ROS 2 or MQTT.
Challenges include ensuring reliable communication in disrupted environments (e.g., underground or during storms), balancing computational load across low-power devices, and avoiding algorithmic bottlenecks. Developers must design lightweight decision-making rules to prevent latency in time-sensitive scenarios. Current research focuses on edge computing to process sensor data locally and hybrid swarms that blend human oversight with autonomy. For instance, NASA’s Collaborative SubT framework tests drone swarms in cave rescue simulations, using Wi-Fi mesh networks to maintain connectivity. Future improvements may involve federated learning to refine swarm behavior across missions or energy-efficient pathfinding to extend operational durations. These advancements aim to make SAR operations faster, safer, and less resource-intensive.