Multi-UAV network control through dynamic task allocation: Ensuring data-rate and bit-error-rate support

Abstract

Abstract-A multi-UAV system relies on communications to operate. Failure to communicate remotely sensed mission data to the base may render the system ineffective, and the inability to exchange command and control messages can lead to system failures. This paper describes a unique method to control communications through distributed task allocation to engage under-utilized UAVs to serve as communication relays and to ensure that the network supports mission tasks. The distributed algorithm uses task assignment information, including task location and proposed execution time, to predict the network topology and plan support using relays. By explicitly coupling task assignment and relay creation processes the team is able to optimize the use of agents to address the needs of dynamic complex missions. The framework is designed to consider realistic network communication dynamics including path loss, stochastic fading, and information routing. The planning strategy is shown to ensure that agents support both datarate and interconnectivity bit-error-rate requirements during task execution. System performance is characterized through experiments both in simulation and in outdoor flight testing with a team of three UAVs.

Multiple-Scenario Unmanned Aerial System Control: A Systems Engineering Approach and Review of Existing Control Methods

“…This algorithm is a key method for finding shortest paths for robots and unmanned vehicles and is used for numerous applications. The use of Dijkstra's algorithm can be found in countless applications to UAS path-planning applications [24][25][26][27][28][29].…”

General Form of the Transmission Quality Factor for Cost Functions in UAV Networks

“…In case we have to calculate the cost between two nodes, each of them is with different communication system, we have to use the lowest transmission power (P t ), in that case we take the worst SNR value, this is actually needed in the positioning phase where the communication cost between two nodes is calculated in order to find the best place for one of them, so each of them could be in transmitting mode or in receiving mode [12]. During the routing phase and task allocation mode we only consider the power of the transmitting node as we need to calculate the SNR value at the receiving node [11].…”

“…Both kinds and their related protocols need a cost metric in order to build the routing tables, this metric should be well defined in order to distinguish between different routes (which are radio links) and select the best at each node or relay. Because of that a lot of researches had been conducted in order to find a general formula for the cost function in UAV networks [11]. A contribution of this paper is that it presents a general closed form for the transmission quality factor of the cost function where it could be used in any routing algorithm as a metric for building the routing tables.…”

“…In [11] a study for the dynamic task allocation had been made, it considers only the shadowing factor together with the path loss effect, without taking into consideration the effect of the Rician factor. Table II shows that we can find more than one case with the same path loss and shadowing standard deviation but the BER is different as the Rician factor is different, which means that the quality of the received signal will be different.…”

A trajectory-tracking controller design using L<inf>1</inf> adaptive control for multi-rotor UAVs

“…In particular, multi-rotor aircraft, which are easily maintained, have received much attention owing to their simple structures. In addition, a number of studies pertaining to these aircraft have been conducted, investigating dynamic modeling [1], control laws [2][3][4], and task allocation [5]. The capability of precision position control, as in hovering flight, is a fundamental requirement for helicopter missions.…”