Decentralized Cooperative Control Methods for the Modified Weapon-Target Assignment Problem

Weapon-target assignment is a combinatorial optimization problem in which a set of weapons must selectively engage a set of targets. In its decentralized form, it is also an important problem in autonomous multi-agent robotics. In this work, decentralized methods are explored for a modified weapon-target assignment problem in which weapons seek to achieve a prespecified probability of kill on each target. Three novel cost functions are proposed that, in cases with low agent-to-target ratios, induce behaviors that may be preferable to the behaviors induced by classical cost functions. The performance of these proposed cost functions is explored in simulation of both homogeneous and heterogeneous engagement scenarios using airborne autonomous weapons. Simulation results demonstrate that the proposed cost functions achieve desired behaviors in cases with low agent-to-target ratios where efficient use of weapons is particularly important.