Coordinated flocking of UAVs for improved connectivity of mobile ground nodes

Abstract

Unmanned aerial vehicles (UAV) have been used by the militmy for surveillance and reconnaissance operutions for the past few decades. The recent prol$eratinn of wireless networking technologies enables the equipment of UAVs with wireless transceivers, and that can in turn allow them to communicate with the fnendly ground nodes as well as other UAVs. Since rugged ground terrain can result in significant signal attenuation, the gmund netwurk can be severely partitioned. Huwever the lower propagation lass between ground and airborne nodes can be efectively utilized to connect rhe islands of connectivity on the ground to form a unified ad hoc network. In this paper; we investigate the UAV placement and navigation strategies with the end goal of improving network connectivity. Since the ground nodes can be mobile, a fired placement strategy is either inadequate or wasteful; hence, we propose to use local Jrocking rules that aerial living beings like birds and insects follow, to meet our goals. We show by simulation that a flocking based navigation strategy is adaptive tu the motion of ground nodes and can indeed maintain high connectivity in a mbile ground network.

Underwater Robot Swarms: Challenges and Opportunities

“…Alternatively, the network can be tolerant to breaks by having robots store messages which cannot be transmitted until a route has been established . Interestingly, the high speed at which flying robots can change position also allows them to rapidly reposition themselves so as to optimise communication or repair breaches in the network (Basu et al, 2004;Dixon and Frew, 2009;Hauert et al, 2010b).…”

Evolved swarming without positioning information: an application in aerial communication relay

“…Existing aerial robotic swarms use relative or global positioning information to navigate in their environment using either map-based strategies (Kuiper and NadjmTehrani, 2006;Parunak et al, 2005;Sauter et al, 2005;Elston and Frew, 2008;Flint et al, 2002;Lawrence et al, 2004;Pack and York, 2005;Yang et al, 2005), Reynolds' Flocking (Reynolds, 1987) or Artificial Physics (Spears et al, 2005) approaches (Basu et al, 2004;De Nardi and Holland, 2007;Holland et al, 2005;Kadrovach and Lamont, 2001;Merino et al, 2006), or predefined swarm formations (Vincent and Rubin, 2004). Other researchers have explored the use of artificial evolution to automatically determine position-aware swarm controllers (Gaudiano et al, 2005;Lin et al, 2004;Richards et al, 2005;Soto and Lin, 2005;Wu et al, 1999).…”

Trajectory Planning for Communication Relay Unmanned Aerial Vehicles in Urban Dynamic Environments

“…Mobile scenarios remains largely unexplored area and a few approaches were considered in the literature so far. Basu et al [8] combined the number of connections to each UAV with a flocking algorithm to maximise the number of connected nodes at any given time. Approaches from [9,10] tested several communication metrics to find the best position or trajectory for a team of UAVs.…”