Control of swarming robots in an area with dead-end passage

This paper addresses the adaptive flocking problem for a swarm of autonomous mobile robots. From the observation of individual and emergent behaviors of fish schools, we propose an adaptive navigation algorithm, enabling large-scale robot swarms with limited sensing capabilities to navigate toward a goal. The proposed algorithm is based on our geometric local interaction which allows three neighboring robot to locally form an equilateral triangle lattice. During traveling in an unknown environment, mobile robots maintain the local formation. According to environmental conditions, this algorithm allows the swarm of robots to split into multiple groups or merge with other groups. More specifically, a group of robots could be trapped in a dead-end passage, but they merge with an adjacent group to escape from the dead-end passage. Accordingly, through the local motions of individual robots, the whole swarm network can be easily recovered in an emergent way. We validate the effectiveness and scalability of the proposed flocking control algorithm by performing various simulations.