Cooperative detection of multiple targets by the group of mobile agents

The paper considers the detection of multiple targets by a group of mobile robots that perform under uncertainty. The agents are equipped with sensors with positive and non-negligible probabilities of detecting the targets at different distances. The goal is to define the trajectories of the agents that can lead to the detection of the targets in minimal time. The suggested solution follows the classical Koopman's approach applied to an occupancy grid, while the decision-making and control schemes are conducted based on information-theoretic criteria. Sensor fusion in each agent and over the agents is implemented using a general Bayesian scheme. The presented procedures follow the expected information gain approach utilizing the "center of view" and the "center of gravity" algorithms. These methods are compared with a simulated learning method. The activity of the procedures is analyzed using numerical simulations.