Dynamic Routing and Coordination of Cluster for Unmanned Aerial Vehicle (UAV) Swarms

Abstract

The flying networks provide an efficient solution for a wide range of military and commercial purposes. The demand for portable and flexible communication is directed towards a quick growth in interaction among unmanned aerial vehicles (UAVs). Due to the frequent change in topology and high mobility of vehicles, routing and coordination becomes a challenging task. To maximize the throughput of the network, this study addresses the UAV swarm’s problems related to the coordination and routing and defines the proposed solution to solve these issues. For this, a network is assumed which contains an equal number of dynamic vehicles. It also presents the communication graph concept of UAVs and designs a fixed-wing UAV model to improve the efficiency of the network in terms of throughput. Furthermore, the proposed algorithm based on Cauchy particle swarm optimization (CPSO) aims towards the better performance of UAV swarms and aims to solve the combinatorial problem. The simulation results show and confirm the performance of the proposed algorithm.

Ground Risk Assessment of UAV Operations Based on Horizontal Distance Estimation under Uncertain Conditions

“…Drag coefficient here expressed as C D is also subject to a stochastic process to better incorporate the uncertainties usually in the drag force during the UAV's descending process. e horizontal distance (x 1 ) from failure point to the highest altitude can then be determined by (2). In this equation below v x,i is the initial horizontal speed of UAV which is also a stochastic variable that is similar to the initial vertical speed.…”

“…Unmanned aerial vehicle (UAV) is the fastest growing sector of the aviation industry, which has become the focus of many commercial and civilian applications including delivery of goods, search and rescue, disaster relief, infrastructure monitoring, precision agriculture, public safety, and weather monitoring [1][2][3].…”

Autonomous Anticollision Decision and Control Method of UAV Based on the Optimization Theory

“…In solving the multi constraint anticollision problem, the model predictive control method has a good e ect [13]. References [14,15] established an anticollision control algorithm under nonlinear constraints in two-dimensional environment by comprehensively using geometric method and model predictive control method and based on the tangent principle. Literature [16] proposed a multi UAV cooperative anticollision method under nonlinear dynamic constraints by using distributed model predictive control, but the premise of this method is that all obstacles are cooperative and their states are accurately known.…”