  Unmanned systems (USs) including unmanned aerial vehicles, unmanned
underwater vehicles, and unmanned ground vehicles have great application
prospects in military and civil fields, among which the process of finding
feasible and optimal paths for the agents in USs is a kernel problem.
Traditional path finding algorithms are hard to adequately obtain optimal paths
in real-time under fast time-varying and poor communication environments. We
propose an online optimal control algorithm for USs based on a one-way
broadcast communication mode under the assumption of a poor communication
environment, mobile targets, radars (or sonar), and missiles (or torpedoes).
With the principle of receding horizon control, optimal (or suboptimal) paths
are then generated by the approximation theory of neural networks and gradient
optimization techniques, with low computation requirements. Also, we give a
convergence analysis for our algorithm, and show that each agent can reach its
target in finite time under some conditions on agents, targets and
radar-missiles. Moreover, simulations demonstrate that the agents in USs can
generate optimal (or suboptimal) paths in real time using our algorithm while
effectively avoiding collision with other agents or detection by enemy radars.
