We address the problem of progressively deploying a set of robots to a formation defined as a point cloud, in a decentralized manner. To achieve this, we present an algorithm that transforms a given point cloud into an acyclic directed graph. This graph is used by the control law to allow a swarm of robots to progressively form the target shape based only on local decisions. This means that free robots (i.e., not yet part of the formation) find their location based on the perceived location of the robots already in the formation. We prove that for a 2D shape it is sufficient for a free robot to compute its distance from two robots in the formation to achieve this objective. We validate our method using physics-based simulations and robotic experiments, showing consistent convergence and minimal formation placement error.

The advent of the swarm makes it feasible to dynamically monitor a wide area for maritime applications. The crucial problems of underwater swarm monitoring are communication and behavior coordination. To tackle these problems, we propose a wide area monitoring strategy that searches for static targets of interest simultaneously. Traditionally, an underwater robot adopts either acoustic communication or optical communication. However, the former is low in bandwidth and the latter is short in communication range. Our strategy coordinates underwater robots through indirect communication, which is inspired by social insects that exchange information by pheromone. The indirect communication is established with the help of a set of underwater communication nodes. We adopt a virtual pheromone-based controller and provide a set of rules to integrate the area of interest into the pheromone. Based on the information in the virtual pheromone, behavior laws are developed to guide the swarm to monitor and search with nearby information. In addition, a robot can improve its performance when using additional far-away pheromone information. The monitoring strategy is further improved by adopting a swarm evolution scheme which automatically adjusts the visiting period. Experimental results show that our strategy is superior to the random strategy in most cases.

This paper presents a distributed, energy-aware method for the autonomous deployment and maintenance of battery-powered robots within a known or unknown region in 2D space. Our approach does not rely on a global positioning system and therefore allows for applications in GPS-denied environments such as underwater sensing or underground monitoring. After covering a region, our system maintains a formation and uses an arbitrary number of charging stations to prevent robots from fully discharging. Analyzing the topology of the network formed during robot deployment, we generate virtual recharging trees which the robots use to navigate toward a nearby charging station when needed. All robots that leave the formation are replaced by their neighbors, maximizing the effective coverage provided by the system. We demonstrate the capability of our methods using models, a physics-based simulator, and experiments with real robots.

This paper presents a distributed, energy-aware algorithm for an autonomous deployment of battery-powered robots in a specified pattern. While each robot gradually discharges and leaves the formation to recharge, the algorithm presented in this paper assures that the formation pattern is preserved. This is achieved by defining the desired pattern as a point cloud where each point is occupied by a robot. The point cloud is transformed into a tree model that is shared among all robots. This model is used by each robot independently to govern its behavior, resulting in a self-adaptive network of robots which automatically generate paths for joining the formation and leaving it to recharge. Robots which leave the formation are replaced by neighbors to preserve the formation pattern. To demonstrate our algorithm we use a physics-based simulator and evaluate the persistence of the pattern formation formed by a robot swarm in an environment without global positioning, using only range and bearing.

This paper presents a synergy pursuit strategy that multiple underwater robots pursue a single moving target based on finite state machine. The processes of the pursuit are defined as seven different states, and to implement each state, the corresponding strategies are designed. As the strategy for each state is implemented by a corresponding formation control, a synergy control law is also designed for multiple underwater robots. In the design of the pursuit strategy, the definition of encirclement is put forward for the underwater robots. Furthermore, the proof that a point is encircled by other points on the plane is given. To solve the problem of the concealment in the tracking and the pursuit, a semicircular pursuit strategy and a circular pursuit strategy are also proposed. Finally, based on this pursuit strategy, the simulation of whole process is carried out with the MOOS(Mission Oriented Operating Suite) platform, and the effectiveness of the semicircular and circular pursuit strategy are verified by lake experiment with practical robots. The results of simulation and lake experiment prove that this pursuit strategy can realize synergy tracking and pursuit for a single moving target under the given conditions.