Multi-robot decentralized exploration using weighted random selection

The exploration tasks using multi-robot systems require efficient coordination and information sharing between robots. The map creation is usually done through allocating frontiers,i.e., the boundary between explored and unknown regions of the map, to each robot. This paper introduces an efficient frontier allocation method based on weighted random selection for a decentralized multi-robot system. The weights are calculated based on the size of the frontiers and the distance between a robot and the frontiers. In this strategy, each robot identifies the available frontiers in a shared map and select the goal for exploration through a random selection. Even though a robot is randomly picking a frontier without coordination with other robots, a collective intelligence is developed at the swarm level. The proposed method is computationally efficient and uses minimal communication between the robots in a decentralized multi-robot system. As a result, the robots get allocated to frontier according to the calculated weight. A comparison with the nearest frontier exploration approach in computerized simulation demonstrates the efficiency of the proposed algorithm.