Adaptive Swarm Formation Control for Hybrid Ground and Aerial Assets

Abstract

The use of Unmanned Aerial Vehicles (UAVs) with Unmanned Ground Vehicles (UGVs) allows for cooperation, coordination, and tight or loose collaboration related to multiple missions. UAVs can provide a global perspective of the surrounding environment, obstacles, and possible threats, broadcasting goals, sub-goals and alterations to the overall mission of the swarm. Further, the deployment of UAVs creates a 3-D sensor network increasing communication capabilities allowing for more complete information about the environment. UAV-UGV coordination has obvious applicability in military applications due to the line of sight issue. Air vehicles can detect items of interest long before UGVs. Related literature in the area refers to general frameworks and simulation results only. In (Chaimowicz and Kumar 2004; Chaimowicz and Kumar 2004), UGV swarms are coordinated and directed by “shepherd” UAVs. A hierarchy is formed between the UAV and the UGVs. UAVs are responsible for grouping and merging swarms as well as controlling swarm distributions and motion. In (Sukhatme, Montgomery et al. 2001), an architecture is proposed for coordinating an autonomous helicopter and a group of UGVs using decentralized controllers. In (Tanner 2007), an approach is proposed to coordinate groups of ground and aerial vehicles for the purpose of locating a moving target in a given area. This is done by combining decentralized flocking algorithms with navigation functions. Other instances utilizing coordination between air and ground vehicles can be seen in (Elfes, Bergerman et al. 1999; Lacroix, Jung et al. 2001; Stentz, Kelly et al. 2002). In this work, the problem of controlling and coordinating heterogeneous unmanned systems required to move as a group is addressed. A strategy is proposed to coordinate groups of Unmaned Ground Vehicles with one or more Unmanned Aerial Vehicles (UAVs). UAVs can be utilized in one of two ways: (1) as alpha robots to guide and oversee the UGVs; and (2) as beta robots to surround the UGVs and adapt accordingly. In the first approach, the UAV guides a swarm of UGVs controlling their overall formation. In the second approach, the 17