Cooperative path planning and task assignment for unmanned air vehicles

Abstract This article presents a review and a novel approach to the decentralized path planning and task assignment for multiple cooperative unmanned air systems, in multiple target, and multiple task environment. The vehicles (or agents) may have complete or partial a priori information about the targets that populate the scenario. Each vehicle autonomously computes the cost for servicing each task available at each target using a path planning algorithm, taking into account obstacles, pop-up threats, and weights the total path cost including potential risk areas. Vehicles assign an initial ranking to each task, and then exchange their ranking information with the others. Each agent then updates the ranking of its tasks using a non-linear dynamic programming algorithm that is proven to be stable and to converge to an equilibrium point where each vehicle is assigned to a different task. The ranking dynamics is initially formulated as a continuous time system, and then time-discretized depending on available data, and transmission rate among the network. Stability of the network and independence of steady-state values from the data rate are evaluated analytically, and via simulation.