Decentralized Bayesian negotiation for cooperative search

Abstract

This paper addresses the problem of coordinating a team of multiple heterogeneous sensing platforms searching for a single lost target. In this approach, the utility of a control sequence is a function of the probability density function (PDF) of the target state. Each decision maker builds an equivalent estimate of this PDF by communicating and fusing the information from the other sensor nodes. Coupled utilities incite the agents to collaborate and to agree on the next best set of actions. Decentralized cooperative planning is achieved via anonymous negotiation based on communication of expected observed information. Simulation results demonstrate the efficiency of the cooperative trajectories for a team of autonomous airborne search vehicles.