Collective Perception in a Robot Swarm

In swarm robotics, hundreds or thousands of robots have to reach a common goal autonomously. Usually, the robots are small and their abilities are very limited. The autonomy of the robots requires that the robots' behaviors are purely based on their local perceptions, which are usually rather limited. If the robot swarm is able to join multiple instances of individual perceptions to one big global picture (e.g. to collectively construct a sort of map), then the swarm can perform efficiently and such a swarm can target complex tasks. We here present two approaches to realize 'collective perception' in a robot swarm. Both require only limited abilities in communication and in calculation. We compare these strategies in different environments and evaluate the swarm's performance in simulations of fluctuating environmental conditions and with varying parameter settings.

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