In this paper the problem of positioning a team of mobile robots for a surveillance task in a non-convex environment with obstacles is considered. The robots are equipped with global positioning capabilities (for instance they are equipped with GPS) and visual sensors able to monitor the surrounding environment. The goal is to maximize the area monitored by the team, by identifying the best configuration of the team members. Due to the non-convex nature of the problem, an analytical solution cannot be obtained. The proposed method is based on a new cognitive-based, adaptive optimization algorithm (CAO). This method allows getting coordinated and scalable controls to accomplish the task, even when the obstacles are unknown. Extensive simulations are presented to show the efficiency of the proposed approach.

In this paper we study the problem of deploying a team of flying robots to perform surveillance coverage missions over an unknown terrain of arbitrary morphology. In such a mission, the robots should simultaneously accomplish two objectives: firstly, to make sure that the overall terrain is visible by the team and, secondly, that the distance between each point in the terrain and one of the robots is as small as possible. These two objectives should be efficiently fulfilled given the physical constraints and limitations imposed at the particular coverage application (i.e., obstacle avoidance, limited sensor capabilities, etc). As the terrain's morphology is unknown and it can be quite complex and non-convex, standard multi-robot coordination and control algorithms are not applicable to the particular problem treated in this paper. In order to overcome such a problem, a new approach that is based on the Cognitive-based Adaptive Optimization (CAO) algorithm is proposed and evaluated in this paper. Both rigorous mathematical arguments and extensive simulations on unknown terrains establish that the proposed approach provides an efficient methodology that can easily incorporate any particular constraints and quickly and safely navigate the robots to an arrangement that optimizes surveillance coverage.