Research Projects

As the potential for societal integration of multi-agent robotic systems increases, the need to manage the collective behaviors of such systems also increases. Agent-agent interactions in a swarm of small unmanned aerial systems (sUAS) lead to the emergence of collective behaviors that enable effective coverage and exploration across large spatial extents. However, the same inherent collective behaviors can occasionally limit the ability of the sUAS swarm to focus on specific objects of interest during coverage or exploration missions. Our work has focused on creating macroscopic models and fine-tuned intuitive interfaces for a human supervisor to influence or guide an sUAS swarm with dynamic levels of incursion on decentralized control afforded by these systems. With the objective of creating more predictable behaviors, this approach can enable the fully utilization of swarm capabilities, while also retaining an ongoing macroscopic-level of swarm control. We demonstrated this capability through experiments in a virtual reality environment by using an impedance control-inspired method to guide 16 drones through a canyon.