Optimal placement of UV-based communications relay nodes

Abstract

We consider a constrained optimization problem with mixed integer and real variables. It models optimal placement of communications relay nodes in the presence of obstacles. This problem is widely encountered, for instance, in robotics, where it is required to survey some target located in one point and convey the gathered information back to a base station located in another point. One or more unmanned aerial or ground vehicles (UAVs or UGVs) can be used for this purpose as communications relays. The decision variables are the number of unmanned vehicles (UVs) and the UV positions. The objective function is assumed to access the placement quality. We suggest one instance of such a function which is more suitable for accessing UAV placement. The constraints are determined by, firstly, a free line of sight requirement for every consecutive pair in the chain and, secondly, a limited communication range. Because of these requirements, our constrained optimization problem is a difficult multi-extremal problem for any fixed number of UVs. Moreover, the feasible set of real variables is typically disjoint. We present an approach that allows us to efficiently find a practically acceptable approximation to a global minimum in the problem of optimal placement of communications relay nodes. It is based on a spatial discretization with a subsequent reduction to a shortest path problem. The case of a restricted number of available UVs is also considered here. We introduce two label correcting algorithms which are able to take advantage of using some peculiarities of the resulting restricted shortest path problem. The algorithms produce a Pareto solution to the two-objective problem of minimizing the path cost and the number of hops. We justify their correctness. The presented results of numerical 3D experiments show that our algorithms are superior to the conventional Bellman-Ford algorithm tailored to solving this problem.

Optimal Placement of CRNs in Manned/ Unmanned Aerial Vehicle Cooperative Engagement System

“…Actually, in the field of communication relay planning, the optimization factors generally included number of relay nodes [17], connectivity [18,19], capacity [20,21], and safety [22]. However, few of literatures once in consideration of comprehensive objectives, besides, there were a few differences between MAV/UAV cooperative engagement system and other fields, i.e., (1) the communication capacities of MAVs and UAVs are different, (2) the dynamic character of UAVs is stronger than that of MAVs, (3) the communication functions of MAVs and UAVs are different.…”

Generating UAV communication networks for monitoring and surveillance

“…However, k * max is the maximum number of UAVs required to reach any target, and k * max N typically applies. For more details and proofs, the reader is referred to [10], [11].…”

Optimal Beamforming and Performance Analysis of Wireless Relay Networks with Unmanned Aerial Vehicle

“…de Freitas et al [5] have considered a scenario of employing multiple UAVs to relay signals from isolated ground nodes to a base station (BS), and developed a scheme to maintain the connectivity of the links between the nodes and the BS. Burdakov et al [6] have presented label correcting algorithms to find a Pareto solution for the two-objective problem to minimize the path cost and the number of UAVs. By using graph search, Burdakov et al [7] have proposed an algorithm to generate Pareto-optimal relay chains so that the good trade-off between the number of UAVs and the quality of the chain can be yielded.…”