Persistent surveillance by heterogeneous multi-agents using mutual information based on observation capability

Using many agents with different characteristics is more effective than using a homogeneous agent to observe a large environment persistently. This study focuses on the heterogeneity of agents’ observation capabilities, such as sensor resolution, by representing these differences through probabilistic observation. This representation allows agents to compute mutual information when selecting surveillance areas and move to where they can obtain the most information from their observations. In addition, we introduce confidence decay for three or more states, a strategy to encourage agents to revisit locations that have not been observed for an extended period of time. Confidence decay represents a gradual decrease in the estimates’ reliability since the state may have changed during the unobserved period. This strategy increases the mutual information of locations that have not been observed for a long time so that the agents will move toward them. Simulations in a changing environment show that the proposed method enables heterogeneous multi-agents to perform persistent surveillance according to their observation capabilities. It also outperforms the existing partition and sweep method in a quantitative comparison of observation accuracy.