Strategic Sacrifice: Collaborative Localization

Imagine a future where small robotic teams inspect our infrastructure—bridges, pipelines, buildings, and satellites—finding leaks and cracks promptly. To conduct effective inspections, however, the robots must first know precisely where they are on these structures, a problem known as localization. This task consumes a significant portion of a robot's computational resources. Our project delves into how collaboration among robots can streamline this process, freeing up more computational resources for inspection tasks. Drawing inspiration from nature's collaborative strategies, such as animals taking turns watching for predators, we introduce a novel mechanism where robots self-organize into dedicated localizers or inspectors, optimizing group inspection productivity. What's remarkable is that this sacrifice, with some robots bearing the computational burden of localization for the group, is entirely self-organized. The robots autonomously decide their roles, adapting to changing environments seamlessly. This approach, validated through theoretical models, simulations, and hardware experiments, showcases effective inspection in dynamic environments, highlighting the potential of autonomous, self-organized robots in making our infrastructure safer.