Reducing sensitivity to localization error through local search

Many researchers study the search of unknown areas with teams of cooperating robots. Robot cooperation often relies upon shared representations built from shared information. When robots share information, they inadvertently share the error associated with that information. In this research, we realistically assume that all position-based data has some amount of localization error. We model the propagation of this error in cooperative search. We conjecture that propagation of error differs based on whether search targets are shared or locally discovered. Search errors due to inaccurate position estimates are quantified and compared in both approaches via simulations.