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Swarm robotics is an approach to collective robotics that takes inspiration from the self-organized behaviors of social animals. Through simple rules and local interactions, swarm robotics aims at designing robust, scalable, and flexible collective behaviors for the coordination of large numbers of robots. In this paper, we analyze the literature from the(More)
We consider a heterogeneous swarm consisting of aerial and wheeled robots. We present a system that enables spatially targeted communication. Our system enables aerial robots to establish dedicated communication links with individual wheeled robots or with selected groups of wheeled robots based on their position in the environment. The system does not rely(More)
We present a novel multi-robot simulator named ARGoS. ARGoS is designed to simulate complex experiments involving large swarms of robots of different types. ARGoS is the first multi-robot simulator that is at the same time both efficient (fast performance with many robots) and flexible (highly customizable for specific experiments). Novel design choices in(More)
— We present ARGoS, a novel open source multi-robot simulator. The main design focus of ARGoS is the real-time simulation of large heterogeneous swarms of robots. Existing robot simulators obtain scalability by imposing limitations on their extensibility and on the accuracy of the robot models. By contrast, in ARGoS we pursue a deeply modular approach that(More)
Collective decision-making is a process whereby the members of a group decide on a course of action by consensus. In this paper, we propose a collective decision-making mechanism for robot swarms deployed in scenarios in which robots can choose between two actions that have the same effects but that have different execution times. The proposed mechanism(More)
In collective transport, a group of robots has to cooperate in order to transport an object. Collective transport is necessary when transporting the object is hard or impossible for a single robot. The task is particularly difficult when communication bandwidth is limited, there is no access to global information or when using a decentralized approach. In(More)
In flocking, a swarm of robots moves cohesively in a common direction. Traditionally, flocking is realized using two main control rules: proximal control, which controls the cohesion of the swarm using local range-and bearing information about neighboring robots; and alignment control, which allows the robots to align in a common direction and uses more(More)
We propose a novel communication strategy inspired by explicit signaling mechanisms seen in vertebrates, in order to improve performance of self-organized flocking for a swarm of mobile robots. The communication strategy is used to make the robots match each other's headings. The task of the robots is to coordinately move towards a common goal direction,(More)
Reinforcement Learning research is traditionally devoted to solve single-task problems. This means that, anytime a new task is faced, learning must be restarted from scratch. Recently , several studies have addressed the issues of reusing the knowledge acquired in solving previous related tasks by transferring information about policies and value functions.(More)