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A group of robot explorers is like a team of cooks tasting the same dish. Each samples their own portion and shares an impression: clear, occupied, or uncertain. But tastes are subjective — one might miss a chair leg, another might only lick the edge. Instead of averaging the discord, the robots agree: where confidence is low, honestly write 'don't know.' To do this, they exchange an e-value — a measure of their own uncertainty (how doubtful their report is). Pooling all these 'doubt numbers,' they get an overall picture where reliability is mathematically guaranteed — say, 95%. The more 'cooks' and the more often they check, the fewer dark spots remain in the recipe. The method doesn't depend on who talks to whom or how bad the sensors are: a robot is allowed to be wrong if it honestly admits it.