Quantifying Swarm Resilience with Simulated Exploration of Maze-Like Environments

Artificial swarms have the potential to provide robust, efficient solutions for a broad range of applications from assisting search and rescue operations to exploring remote planets. However, many fundamental obstacles still need to be overcome to bridge the gap between theory and application. In this characterization work, we demonstrate how a human rescuer can leverage minimal local observations of emergent swarm behavior to locate a lone survivor in a maze-like environment. The simulated robots and rescuer have limited sensing and no communication capabilities to model a worst-case scenario. We then explore the impact of fundamental properties at the individual robot level on the utility of the emergent behavior to direct swarm design choices. We further demonstrate the relative robustness of the simulated robotic swarm by quantifying how reasonable probabilistic failure affects the rescue time in a complex environment. These results are compared to the theoretical performance of a single wall-following robot to further demonstrate the potential benefits of utilizing robotic swarms for rescue operations.