We address the synthesis of controllers for a swarm of robots to generate a desired two-dimensional geometric pattern specified by a simple closed planar curve with local interactions for avoiding collisions or maintaining specified relative distance constraints. The controllers are decentralized in the sense that the robots do not need to exchange or know each other's state information. Instead, we assume that the robots have sensors allowing them to obtain information about relative positions of neighbors within a known range.We establish stability and convergence properties of the controllers for a certain class of simple closed curves.We illustrate our approach through simulations and consider extensions to more general planar curves. KEYWORDS: robot swarms, decentralised control; motion planning. SUMMARYWe address the synthesis of controllers for a swarm of robots to generate a desired two-dimensional geometric pattern specified by a simple closed planar curve with local interactions for avoiding collisions or maintaining specified relative distance constraints. The controllers are decentralized in the sense that the robots do not need to exchange or know each other's state information. Instead, we assume that the robots have sensors allowing them to obtain information about relative positions of neighbors within a known range. We establish stability and convergence properties of the controllers for a certain class of simple closed curves. We illustrate our approach through simulations and consider extensions to more general planar curves.

We address the synthesis of controllers for large groups of robots and sensors, tackling the specific problem of controlling a swarm of robots to generate patterns specified by implicit functions of the form s(x, y) = 0. We derive decentralized controllers that allow the robots to converge to a given curve S and spread along this curve. We consider implicit functions that are weighted sums of radial basis functions created by interpolating from a set of constraint points, which give us a high degree of control over the desired 2D curves. We describe the generation of simple plans for swarms of robots using these functions and illustrate. Abstract-We address the synthesis of controllers for large groups of robots and sensors, tackling the specific problem of controlling a swarm of robots to generate patterns specified by implicit functions of the form s(x, y) = 0. We derive decentralized controllers that allow the robots to converge to a given curve S and spread along this curve. We consider implicit functions that are weighted sums of radial basis functions created by interpolating from a set of constraint points, which give us a high degree of control over the desired 2D curves. We describe the generation of simple plans for swarms of robots using these functions and illustrate our approach through simulations and real experiments.

In this paper, we present the component technologies and the integration of these technologies for the development of an adaptive system of heterogeneous robots for urban surveillance. In our integrated experiment and demonstration, aerial robots generate maps that are used to design navigation controllers and plan missions for the team. A team of ground robots constructs a radio signal strength map that is used as an aid for planning missions. Multiple robots are to establish a mobile, ad-hoc communication network that is aware of the radio signal strength between nodes and adapts to changing conditions to maintain connectivity. Finally, the team of aerial and ground robots is able to monitor a small village, and search for and localize human targets by the color of the uniform, while ensuring that the information from the team is available to a remotely located human operator. The key component technologies and contributions include (a) mission specification and planning software; (b) decentralized control for navigation in an urban environment while maintaining communication; (c) programming abstractions and composition of controllers for multi-robot deployment; (d) cooperative control strategies for search, identification, and localization of targets; and (e) three-dimensional mapping in an urban setting.

-We address the problem of deploying groups of tens or hundreds of unmanned ground vehicles (UGVs) in urban environments where a group of aerial vehicles (UAVs) can be used to coordinate the ground vehicles. We envision a hierarchy in which UAVs with aerial cameras can be used to monitor and command a swarm of UGVs, controlling the splitting and merging of the swarm into groups and the shape (distribution) and motion of each group. We call these UAVs Aerial Shepherds. We show a probabilistic approach using the EM algorithm for the initial assignment of shepherds to groups and present behaviors that allow an efficient hierarchical decomposition. We illustrate the framework through simulation examples, with applications to deployment in an urban environment.