A probabilistic finite state machine based strategy for multi-target search using swarm robotics

As a distributed system, swarm robotics is well suited for the multi-target search task where a single robot is rather inefficient. In this paper, a model of the multi-target search problem in swarm robotics and its approximate mathematical representation are given, based on which a lower bound of the expected number of iterations is drawn. Two categories of behavior-based strategies for target search are introduced: one is inspired from swarm intelligence optimization while the other from random walk. A novel search strategy based on probabilistic finite state machine is put forward, showing the highest efficiency in all presented algorithms, which is very close to the optimal value in situations with a large number of robots. It has been demonstrated by extensive experiments that the novel strategy has excellent stability, striking a good balance between exploration and exploitation, as well as a good trade-off between parallelism and cooperative capability.