This paper addresses the self-assembly of a large team of autonomous boats into floating platforms. We describe the design of individual boats, the systems concept, the algorithms, the software architecture and experimental results with prototypes that are 1:12 scale realizations of modified ISO shipping containers, with the goal of demonstrating self-assembly into large maritime structures such as air strips, bridges, harbors or sea bases. Each container is a robotic module capable of holonomic motion that can dock in a brick pattern to form arbitrary shapes. Over 60 modules were built of varying capability. The docking mechanism is designed to be robust to large disturbances that can be expected in the high seas. The docking mechanism also incorporates adjustable stiffness so that the conglomerate can comply to waves representative of sea state three, and have the ability to dynamically stiffen as required. The component modules for autonomous assembly, docking and simultaneous collision-free planning as well as the software architecture are presented along with the description of experimental verification.

This paper considers the problem of decentralized goal assignment and trajectory generation for multi-robot networks when only local communication is available, and proposes an approach based on methods related to switched systems and set invariance. A family of Lyapunov-like functions is employed to encode the (local) decision making among candidate goal assignments, under which the agents pick the assignment which results in the shortest total distance to the goals. An additional family of Lyapunov-like barrier functions is activated in the case when the optimal assignment may lead to colliding trajectories, thus maintaining system safety while preserving the convergence guarantees. The proposed switching strategies give rise to feedback control policies which are scalable as the number of agents increases, and therefore are suitable for applications including first-response deployment of robotic networks under limited information sharing. Simulations demonstrate the efficacy of the proposed method.

This paper considers the problem of finding optimal time parameterized trajectories for N unlabeled robots navigating through a cluttered environment to N unlabeled goal locations where success is defined as every goal being reached by any robot. We propose a complete computationally-tractable algorithm for simultaneously finding trajectories and assignment of goal locations. This method is then demonstrated to have an upper complexity bound of that scales polynomially in the number of robots, O(N3). The trajectories generated are guaranteed to be minimum length and collision free, while the assignment policy minimizes the maximum distance travelled. The key idea in the paper comes from the coupling between the optimal assignment, the properties of the resulting paths, and the set of valid priority assignments to the robots. These benefits result from structure in the solution to the optimal assignment to create a partial ordering of the robots, which in turn allows safe trajectories to be easily generated. Finally, we demonstrate the performance of the algorithm through simulations with tens and hundreds of robots operating in cluttered and confined environments.

We address formation control for a team of quadrotors in which the robots follow a specified group trajectory while safely changing the shape of the formation according to specifications. The formation is prescribed by shape vectors which dictate the relative separations and bearings between the robots, while the group trajectory is specified as the desired trajectory of a leader or a virtual robot in the group. Each robot plans its trajectory independently based on its local information of neighboring robots which includes both the neighbor's planned trajectory and an estimate of its state. We show that the decentralized trajectory planners (a) result in consensus on the planned trajectory for predefined shapes and (b) achieve safe reconfiguration when changing shapes.