We propose a method for controlling multiple active drifters in the presence of external forcing induced by the ocean. Our active drifters have one actuator: they can lower and raise their drogues in depth. By exploiting the vertically stratified nature of ocean currents, we show how classical multi-robot tasks (spreading out and aggregation) can be accomplished by the multi-drifter system. Tests with a realistic simulation based on an ocean model suggest that a practical implementation of active drifters which aggregate and disperse in the coastal ocean could be realized through our control method with relatively inexpensive components. Specifically, we are able to show that over a 90 day deployment a significant fraction of drifters can be made to aggregate in few clusters suitable for recovery.

In this paper a method for distributed reciprocal collision avoidance among multiple non-holonomic robots with bike kinematics is presented. The proposed algorithm, bicycle reciprocal collision avoidance (B-ORCA), builds on the concept of optimal reciprocal collision avoidance (ORCA) for holonomic robots but furthermore guarantees collision-free motions under the kinematic constraints of car-like vehicles. The underlying principle of the B-ORCA algorithm applies more generally to other kinematic models, as it combines velocity obstacles with generic tracking control. The theoretical results on collision avoidance are validated by several simulation experiments between multiple car-like robots.

This paper presents a fully automated method to display objects and animations in 3D with a group of aerial vehicles. The system input is a single object or an animation (sequence of objects) created by an artist. The first stage is to generate physical goal configurations and robot colors to represent the objects with the available number of robots. The run-time system includes algorithms for goal assignment, path planning and local reciprocal collision avoidance that guarantee smooth, fast and oscillation-free motion. The presented algorithms are tested in simulations and verified with real quadrotor helicopters and scale to large robot swarms.

This paper describes work on multi-robot pattern formation. Arbitrary target patterns are represented with an optimal robot deployment, using a method that is independent of the number of robots. Furthermore, the trajectories are visually appealing in the sense of being smooth, oscillation free, and showing fast convergence. A distributed controller guarantees collision free trajectories while taking into account the kinematics of differentially driven robots. Experimental results are provided for a representative set of patterns, for a swarm of up to ten physical robots, and for fifty virtual robots in simulation.

DisCoverage is a distributed strategy for frontier-based multi-robot exploration. The robots coordinate by a partition of the environment, and choose their target points by optimizing a locally decomposable objective function. In [9] DisCoverage for convex regions was proposed. In this work, we extend DisCoverage to support arbitrary non-convex real-world environments with obstacles. Therefore, we introduce a transformation of non-convex environments to robot centric star-shaped domains. This results in a general solution with broader applications for exploration and path planning. Simulations as well as experiments with real robots demonstrate the exploration progress.

The objective of this paper is the full 6D relative localization of mobile devices, and direct robot-robot localization in particular. We present a novel relative localization system that consists of two complementary modules: a monocular vision module and a target module with four active or passive markers. The core localization algorithm running on the modules determines the marker positions in the camera image and derives the relative robot pose in 3D space. The system is supported by a prediction mechanism based on regression. The modules are tested successfully in experiments with a quadrotor helicopter as well as on a team of two e-puck robots performing a coverage task. The relative localization system provides accuracies of a few centimeters in position and up to a few degrees in orientation. Furthermore, the system is lightweight, with low complexity and system requirements, which enables its application to a wide range of mobile robot platforms.

This paper presents a solution to decentralized Voronoi coverage in non-convex polygonal environments. We show that complications arise when existing approaches to Voronoi coverage are applied for deploying a group of robots in non-convex environments. We present an algorithm that is guaranteed to converge to a local optimum. Our algorithm combines classical Voronoi coverage with the Lloyd algorithm and the local path planning algorithm TangentBug to compute the motion of the robots around obstacles and corners. We present the algorithm and prove convergence and optimality. We also discuss experimental results from an implementation with five robots.

This paper addresses the problem of deploying a group of networked robots on a non-planar surface embedded in 3D space. Two distributed coverage control algorithms are presented that both provide a solution to the problem by discrete coverage of a graph. The first method computes shortest paths and runs the Lloyd algorithm on the graph to obtain a centroidal Voronoi tessellation. The second method uses the Euclidean distance measure and locally exchanges mesh cells between approximated Voronoi regions to reach an optimal robot configuration. Both methods are compared and evaluated in simulations and in experiments with five robots on a curved surface.