The rapid proliferation of affordable unmanned aerial systems has enabled their deployment in fields ranging from cinematography to disaster response. While large-scale drone swarms have become a familiar sight in light shows and demonstrations, their underlying coordination has typically relied on preprogrammed sequences crafted by animators and refined through extensive computer simulations. This approach, while visually precise, lacks the adaptability seen in natural collective behaviors.
At the University of Houston, Aaron Becker, associate professor of electrical and computer engineering, is leading a research effort to change that paradigm. Supported by a $1.7 million grant from the Kostas Research Institute at Northeastern University, LLC, Becker’s team is developing algorithms that draw inspiration from the decentralized decision-making observed in bird flocks and fish schools. “These movements are not pre-programmed but are based on local decisions by individual birds or fish,” Becker explained. By embedding similar local decision-making capabilities into drones, the team aims to create swarms that can dynamically adapt to changing conditions without relying solely on pre-scripted instructions.
The project brings together expertise from multiple disciplines. Alongside Becker are David Jackson, professor of electrical and computer engineering; Julien Leclerc, assistant research professor of electrical and computer engineering; and Daniel Onofrei, associate professor of mathematics. This collaboration reflects the complexity of the challenge: merging real-time computation on individual drones with strategic oversight from human operators.
“The majority of current research on swarms follows the same pattern and either relies on offline computation or uses simple rule-based logic such as ‘don’t bump into your neighbor while following the leader.’ Computers are great at fast computation and implementing tactics, but humans can excel at strategic decision making. We want to combine these,” Becker said. The envisioned system would allow drones to process environmental data locally, share that information across the swarm, and present synthesized visualizations to a human operator. This operator could then make high-level decisions, effectively guiding the swarm’s strategic objectives while the drones handle tactical execution.
Two initial application scenarios will serve as testbeds for the technology. In the first, a swarm will perform aerial sensing during a forest fire. The drones must track the fire’s spread while simultaneously acting as communication relays for firefighting teams on the ground. This dual role demands constant adaptation to shifting wind patterns, fire intensity, and terrain obstacles, all while maintaining network connectivity.
The second scenario focuses on aerial security for a commercial facility and campus. Here, drones will escort vehicles entering and exiting the premises. The challenge lies in coordinating coverage while managing each drone’s limited battery life, requiring seamless handoffs and autonomous returns to charging stations. Such operations demand not only precise navigation but also efficient energy management across the swarm.
Becker’s background in swarm robotics provides a foundation for tackling these challenges. His previous work has explored controlling massive groups of robots with minimal instructions, demonstrating that large-scale coordination can emerge from simple, well-designed rules. By integrating this experience with advances in onboard computation and communication, the team seeks to bridge the gap between rigidly choreographed formations and truly adaptive aerial collectives.
The research aligns with broader trends in autonomous systems, where distributed intelligence and human-machine teaming are increasingly seen as critical for complex missions. In fields such as environmental monitoring, infrastructure inspection, and security, the ability to rapidly reconfigure a swarm’s behavior in response to unforeseen events could significantly enhance mission effectiveness. The University of Houston team’s approach—melding local autonomy with centralized strategic input—offers a pathway toward that capability.