Swarm robotics: a review from the swarm engineering perspective <s> Real-robot analysis <s> There is an alternative route to Artificial Intelligence that diverges from the directions pursued under that banner for the last thirty some years. The traditional approach has emphasized the abstract manipulation of symbols, whose grounding in physical reality has rarely been achieved. We explore a research methodology which emphasizes ongoing physical interaction with the environment as the primary source of constraint on the design of intelligent systems. We show how this methodology has recently had significant successes on a par with the most successful classical efforts. We outline plausible future work along these lines which can lead to vastly more ambitious systems. <s> BIB001 </s> Swarm robotics: a review from the swarm engineering perspective <s> Real-robot analysis <s> We describe techniques for coordinating the actions of large numbers of small-scale robots to achieve useful large-scale results in surveillance, reconnaissance, hazard detection, and path finding. We exploit the biologically inspired notion of a “virtual pheromone,” implemented using simple transceivers mounted atop each robot. Unlike the chemical markers used by insect colonies for communication and coordination, our virtual pheromones are symbolic messages tied to the robots themselves rather than to fixed locations in the environment. This enables our robot collective to become a distributed computing mesh embedded within the environment, while simultaneously acting as a physical embodiment of the user interface. This leads to notions of world-embedded computation and world-embedded displays that provide different ways to think about robot colonies and the types of distributed computations that such colonies might perform. <s> BIB002 </s> Swarm robotics: a review from the swarm engineering perspective <s> Real-robot analysis <s> We introduce a framework, called “physicomimetics,” that provides distributed control of large collections of mobile physical agents in sensor networks. The agents sense and react to virtual forces, which are motivated by natural physics laws. Thus, physicomimetics is founded upon solid scientific principles. Furthermore, this framework provides an effective basis for self-organization, fault-tolerance, and self-repair. Three primary factors distinguish our framework from others that are related: an emphasis on minimality (e.g., cost effectiveness of large numbers of agents implies a need for expendable platforms with few sensors), ease of implementation, and run-time efficiency. Examples are shown of how this framework has been applied to construct various regular geometric lattice configurations (distributed sensing grids), as well as dynamic behavior for perimeter defense and surveillance. Analyses are provided that facilitate system understanding and predictability, including both qualitative and quantitative analyses of potential energy and a system phase transition. Physicomimetics has been implemented both in simulation and on a team of seven mobile robots. Specifics of the robotic embodiment are presented in the paper. <s> BIB003 </s> Swarm robotics: a review from the swarm engineering perspective <s> Real-robot analysis <s> Robots are said to be capable of self-assembly when they can autonomously form physical connections with each other. By examining different ways in which a system can use self-assembly (i.e., different strategies), we demonstrate and quantify the performance costs and benefits of (i) acting as a physically larger self-assembled entity, (ii) letting the system choose when and if to self-assemble, (iii) coordinating the sensing and actuation of the connected robots so that they respond to the environment as a single collective entity. Our analysis is primarily based on real world experiments in a hill crossing task. The configuration of the hill is not known by the robots in advance--the hill can be present or absent, and can vary in steepness and orientation. In some configurations, the robots can overcome the hill more quickly by navigating individually, while other configurations require the robots to self-assemble to overcome the hill. We demonstrate the applicability of our self-assembly strategies to two other tasks--hole crossing and robot rescue--for which we present further proof-of-concept experiments with real robots. <s> BIB004
The use of real robots (as opposed to simulated robots) to validate a collective behavior is a fundamental tool. In fact, it is practically unfeasible to simulate all the aspects of reality  BIB001 . Experiments with real robots help to test the robustness of swarm robotics systems that have noisy sensors and actuators. Working with real robots is very important also because it helps discriminating between collective behaviors realizable in practice and those that work only under unrealistic assumptions. It must be noted that in all real-robot experiments presented in the analyzed literature, the experiments are performed in controlled environments. By controlled environments we mean artificial arenas in which most conditions-e.g., light intensity, radio interference and floor smoothness-can be controlled by the experimenter. This is often very far from the scenarios in which swarm robotics systems are supposed to operate. For this reason, realrobot experiments should not be considered as a way to validate collective behaviors for their use in real-world applications, but rather as a way to test them against realistic noise patterns in sensors and actuators. In this review we present more than 60 publications dealing with collective behaviors in the swarm robotics field (see Sect. 3). Slightly more than half of these publications presented results obtained only through simulations or models. We believe that the reason behind this choice is that, in general, it is easier, faster, and safer to perform experiments using models or simulations than using robots. In the papers that included experiments done with real robots, the scope of the use of the robots can be divided in two categories: proof-of-concept experiments and extensive experiments. The first category includes slightly more than half of the analyzed works that involve real-robot experiments. In these works, few runs (typically one) of an experiment with real robots are performed. The aim of real-robot experiments within these works is to show that the proposed collective behavior is realizable. Examples of this kind of experiment can be found in the works by BIB002 and BIB003 . In the other category, instead, several runs are executed and data are gathered to be analyzed for comparison with simulated runs or to show properties of the system. Examples of this kind of experiments can be found in the works by Çelikkanat andŞahin (2010) and BIB004 . Very often in the swarm robotics literature, the reasons why robots are used are not explicitly stated. In particular, the answer to the question "what does the real-robot study add to the simulation or theoretical study" is almost never explicitly answered, as it should. An effort in this sense would simplify the process of reproducing results as it would help to clarify possible differences between the model and the real-robot system. Moreover, clarifying the role of real robots in experiments can help in porting a similar collective behavior to a different robotic hardware.