Adaptive Expected Reactive algorithm for Heterogeneous Patrolling Systems based on Target Uncertainty.

Multi-robot patrolling for dynamic coverage in flat environments is proposed, through a systematic simulative analysis between the Greedy Bayesian Strategy and the Expected Reactive algorithm based on the expected idleness. The two approaches are compared against unreliable communications, communication and sensing range, and number of conflicts. In addition, we introduce a new weighting-term for the regions close to a quantity of interest detected by robots, decreasing the passing-time for those regions. Combining the proposed control strategy and a traditional distributed and recursive Weighted Least Square estimation algorithm, the swarm is capable to compute the quantity of interest position with a desired target uncertainty. Extensive simulations and comparisons are reported.