Application of Thompson Sampling on Multi-target Search

Multi-robot coordination demonstrates outstanding performance in exploring and tracking targets in unknown environments, especially in complex terrain or disaster environments. In these cases, the distribution of the search area is changeable and irregular, and the exploration target may also move in an unknown environment. To quickly determine the location of these exploration targets, we propose a distributed control strategy in the article, which applies Bernoulli Thompson sampling to solve the multi-armed bandit problem to allow multiple robots to share information and efficiently search targets. This algorithm significantly speeds up the robot's search for targets, especially when most targets are concentrated in certain fixed areas of the environment.